Ar anchoring quality of experience (QOE) framework

Abstract

Some embodiments of a method may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; measuring the at least one creation-based QoE metric; measuring at least one detection-based QoE metric; and measuring at least one tracking-based QoE metric. Some embodiments of the method may further include adjusting an AR anchoring process based on at least one of the at least one creation-based QoE metric, the at least one detection-based QoE metric, and the at least one tracking-based QoE metric.

Description

AR ANCHORING QUALITY OF EXPERIENCE (QOE) FRAMEWORKCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims benefit of European Patent Application No. EP23306330, entitled "AR ANCHORING QUALITY OF EXPERIENCE (QOE) FRAMEWORK” and filed August 3, 2023, which is hereby incorporated by reference in its entirety.INCORPORATION BY REFERENCE

[0002] The present application incorporates by reference in their entirety the following applications: World Patent Application Serial No. WO 2023 / 057388, entitled "INTERACTIVE ANCHORS IN AUGMENTED REALITY SCENE GRAPHS” and filed October 3, 2022 ("‘388 application”); European Patent Application Serial No. EP21306731, entitled "INTERACTIVE ANCHORS IN AUGMENTED REALITY SCENE GRAPHS” and filed December 9, 2021 ("731 application”); and European Patent Application Serial No. EP21306409, entitled "INTERACTIVE ANCHORS IN AUGMENTED REALITY SCENE GRAPHS” and filed October 6, 2021 ("‘409 application”).BACKGROUND

[0003] Augmented reality (AR) is a technology enabling interactive experiences in which the real-world environment is enhanced by virtual content, which may be defined across multiple sensory modalities, including visual, auditory, and haptic modalities. During runtime of an application, virtual content (such as 3D content or an audio file) may be rendered in real-time in a way which is consistent with a user context (which may include an environment, point of view, and / or device). Scene graphs (such as the one proposed by Khronos / gITF and its extensions defined in M PEG Scene Description format) are a way to represent content to be rendered. They may combine a declarative description of a scene structure by linking real-environment objects and virtual objects and / or by linking binary representations of the virtual content.SUMMARY

[0004] A first example method in accordance with some embodiments may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one qualityof experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one tracking-based QoE metric.

[0005] For some embodiments of the first example method, the at least one tracking-based QoE metric is based on a static trackable in a user environment, and the static trackable remains in a fixed position within the user environment.

[0006] For some embodiments of the first example method, the at least one tracking-based QoE metric is based on a moving trackable in a user environment, and the moving trackable changes position within the user environment.

[0007] Some embodiments of the first example method may further include obtaining tracking information from a separate user equipment device, wherein measuring the at least one tracking-based QoE metric is based on the tracking information from the separate user equipment device.

[0008] For some embodiments of the first example method, measuring the at least one tracking-based QoE metric comprises using at least one trackable entity, and the at least one trackable object is a model of an element in a real-world user environment.

[0009] For some embodiments of the first example method, measuring the at least one tracking-based QoE metric is based on at least one anchor entity, the at least one anchor entity is a real-world pose, and the method further comprises using the at least one trackable entity to identify the real-world pose corresponding to the at least one anchor entity.

[0010] Some embodiments of the first example method may further include measuring the at least one tracking-based QoE metric comprises measuring a location of a trackable entity within a real-world user environment.

[0011] For some embodiments of the first example method, measuring the location of the trackable entity within the real-world user environment is based on an anchor corresponding to a real-world pose.

[0012] Some embodiments of the first example method may further include measuring a trackable pose prediction error (TPPE), wherein the TPPE corresponds to a difference between a predicted pose of a trackable entity and an actual pose of the trackable entity in a real-world user environment, and wherein measuring the at least one tracking-based QoE metric is based on measuring a pose of the trackable entity.

[0013] Some embodiments of the first example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is needed, cycling back to remeasure the at least one detection-based QoE metric.

[0014] Some embodiments of the first example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is not needed, cycling back to re-measure the at least one tracking-based QoE metric.

[0015] Some embodiments of the first example method may further include adjusting an AR anchoring process.

[0016] For some embodiments of the first example method, adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

[0017] For some embodiments of the first example method, adjusting the AR anchoring process is based on the at least one tracking-based QoE metric.

[0018] For some embodiments of the first example method, adjusting the AR anchoring process comprises selecting a new trackable entity related to measuring the at least one tracking-based QoE metric.

[0019] For some embodiments of the first example method, selecting the new trackable entity comprises selecting the new trackable entity from a list included in a scene description file.

[0020] Some embodiments of the first example method may further include displaying a warning message corresponding to the at least one tracking-based QoE metric.

[0021] For some embodiments of the first example method, the at least one tracking-based QoE metric comprises an Anchor Untracked Ratio (AUR) metric.

[0022] For some embodiments of the first example method, the at least one tracking-based QoE metric comprises a Trackable Pose Prediction Error (TPPE) metric.

[0023] A first example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0024] A second example method in accordance with some embodiments may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one creation-based QoE metric.

[0025] For some embodiments of the second example method, the at least one creation-based QoE metric is an anchor creation delay (ACD) measurement, and measuring the at least one creation-based QoE metric comprises: measuring a creation-based start time; measuring a creation-based end time; and determiningthe at least one creation-based QoE metric based on the creation-based start time and the creation-based end time.

[0026] Some embodiments of the second example method may further include adjusting an AR anchoring process.

[0027] For some embodiments of the second example method, adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

[0028] For some embodiments of the second example method, adjusting the AR anchoring process is based on the creation-based QoE metric.

[0029] Some embodiments of the second example method may further include displaying a warning message corresponding to the at least one creation-based QoE metric.

[0030] For some embodiments of the second example method, the at least one creation-based QoE metric comprises an Anchor Creation Delay (ACD) metric.

[0031] A second example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0032] A third example method in accordance with some embodiments may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one detection-based QoE metric.

[0033] For some embodiments of the third example method, wherein the at least one detection-based QoE metric is an anchor detection to render to photon (ADRP) measurement, and wherein measuring the at least one detection-based QoE metric comprises: measuring a presentation time; measuring an anchor pose request time; and determining the at least one detection -based QoE metric based on the presentation time and the anchor pose request time.

[0034] Some embodiments of the third example method may further include adjusting an AR anchoring process.

[0035] For some embodiments of the third example method, adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

[0036] For some embodiments of the third example method, adjusting the AR anchoring process is based on the at least one detection-based QoE metric.

[0037] Some embodiments of the third example method may further include displaying a warning message corresponding to the at least one detection-based QoE metric.

[0038] For some embodiments of the third example method, the at least one detection-based QoE metric comprises an Anchor Detection-to-Render-to-Photon (ADRP) metric.

[0039] A third example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0040] A fourth example method in accordance with some embodiments may include: requesting an anchor pose; detecting an anchor; updating a scene based on the anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0041] A fourth example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0042] A fifth example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving an anchor pose; updating a scene based on the received anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0043] A fifth example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0044] A sixth example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving a rendered frame; decoding the rendered frame; presenting the decoded frame; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the decoded frame is presented and when an associated anchor pose is requested.

[0045] A sixth example apparatus in accordance with some embodiments may include: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0046] A seventh example apparatus in accordance with some embodiments may include at least one processor configured to perform the method of any one of the methods listed above.

[0047] An eighth example apparatus in accordance with some embodiments may include a computer- readable medium storing instructions for causing one or more processors to perform the method of any one of the methods listed above.

[0048] A ninth example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform the method of any one of the methods listed above.

[0049] An example computer-readable medium in accordance with some embodiments may include storing instructions generated according to any one of the methods listed above.

[0050] An example signal in accordance with some embodiments may include a metric generated according to any one of the methods listed above.

[0051] An example method in accordance with some embodiments may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; measuring the at least one creation-based QoE metric; measuring at least one detection-based QoE metric; and measuring at least one tracking-based QoE metric.

[0052] For some embodiments of the example method, the at least one creation-based QoE metric is an anchor creation delay (ACD) measurement, and measuring the at least one creation-based QoE metric may include: measuring a creation-based start time; measuring a creation-based end time; and determining the at least one creation-based QoE metric based on the creation-based start time and the creation-based end time.

[0053] For some embodiments of the example method, the at least one detection-based QoE metric is an anchor detection to render to photon (ADRP) measurement, and measuring the at least one detection -based QoE metric may include: measuring a presentation time; measuring an anchor pose request time; and determining the at least one detection-based QoE metric based on the presentation time and the anchor pose request time.

[0054] For some embodiments of the example method, the at least one tracking-based QoE metric is based on a static trackable in a user environment, and the static trackable remains in a fixed position within the user environment.

[0055] For some embodiments of the example method, the at least one tracking-based QoE metric is based on a moving trackable in a user environment, and the moving trackable changes position within the user environment.

[0056] Some embodiments of the example method may further include: obtaining tracking information from a separate user equipment device, measuring the at least one tracking-based QoE metric is based on the tracking information from the separate user equipment device.

[0057] For some embodiments of the example method, measuring the at least one tracking-based QoE metric comprises using at least one trackable entity, and the at least one trackable object is a model of an element in a real-world user environment.

[0058] For some embodiments of the example method, measuring the at least one tracking-based QoE metric is based on at least one anchor entity, the at least one anchor entity is a real-world pose, and the method further comprises using the at least one trackable entity to identify the real-world pose corresponding to the at least one anchor entity.

[0059] Some embodiments of the example method may further include measuring the at least one tracking-based QoE metric comprises measuring a location of a trackable entity within a real-world user environment.

[0060] For some embodiments of the example method, measuring the location of the trackable entity within the real-world user environment is based on an anchor corresponding to a real-world pose.

[0061] Some embodiments of the example method may further include: measuring a trackable pose prediction error (TPPE), wherein the TPPE corresponds to a difference between a predicted pose of a trackable entity and an actual pose of the trackable entity in a real-world user environment, and measuring the at least one tracking-based QoE metric is based on measuring a pose of the trackable entity.

[0062] Some embodiments of the example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is needed, cycling back to re-measure the at least one detection-based QoE metric.

[0063] Some embodiments of the example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is not needed, cycling back to re-measure the at least one tracking-based QoE metric.

[0064] Some embodiments of the example method may further include adjusting an AR anchoring process.

[0065] For some embodiments of the example method, adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

[0066] For some embodiments of the example method, adjusting the AR anchoring process is based on at least one of the at least one creation-based QoE metric, the at least one detection-based QoE metric, and the at least one tracking-based QoE metric.

[0067] For some embodiments of the example method, adjusting the AR anchoring process comprises selecting a new trackable entity related to measuring the at least one tracking-based QoE metric.

[0068] For some embodiments of the example method, selecting the new trackable entity comprises selecting the new trackable entity from a list included in a scene description file.

[0069] Some embodiments of the example method may further include displaying a warning message corresponding to at least one of the at least one creation-based QoE metric, the at least one detection-based QoE metric, and the at least one tracking-based QoE metric.

[0070] An example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0071] A second example method in accordance with some embodiments may include: requesting an anchor pose; detecting an anchor; updating a scene based on the anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0072] A second example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0073] A third example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving an anchor pose; updating a scene based on the received anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0074] A third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0075] A fourth example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving a rendered frame; decoding the rendered frame; presenting the decoded frame; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the decoded frame is presented and when an associated anchor pose is requested.

[0076] A fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0077] An example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the methods listed above.

[0078] An example apparatus in accordance with some embodiments may include a computer-readable medium storing instructions for causing one or more processors to perform any one of the methods listed above.

[0079] An example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the methods listed above.

[0080] An example computer-readable medium in accordance with some embodiments may store a scene description file generated according to any one of the methods listed above.

[0081] An example signal in accordance with some embodiments may include a scene description file generated according to any one of the methods listed above.

[0082] In additional embodiments, encoder and decoder apparatus are provided to perform the methods described herein. An encoder or decoder apparatus may include a processor configured to perform the methods described herein. The apparatus may include a computer-readable medium (e.g. a non-transitory medium) storing instructions for performing the methods described herein. In some embodiments, a computer-readable medium (e.g. a non-transitory medium) stores a video encoded using any of the methods described herein.

[0083] One or more of the present embodiments also provide a computer readable storage medium having stored thereon instructions for performing bi-directional optical flow, encoding or decoding video data according to any of the methods described above. The present embodiments also provide a computer readable storage medium having stored thereon a bitstream generated according to the methods described above. The present embodiments also provide a method and apparatus for transmitting the bitstreamgenerated according to the methods described above. The present embodiments also provide a computer program product including instructions for performing any of the methods described.BRIEF DESCRIPTION OF THE DRAWINGS

[0084] FIG. 1A is a system diagram illustrating an example communications system according to some embodiments.

[0085] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to some embodiments.

[0086] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments.

[0087] FIG. 2A is a functional block diagram of block-based video encoder, such as an encoder used for Versatile Video Coding (VVC), according to some embodiments.

[0088] FIG. 2B is a functional block diagram of a block-based video decoder, such as a decoder used for WC, according to some embodiments.

[0089] FIG. 3A is a schematic illustration showing an example AR anchoring according to some embodiments.

[0090] FIG. 3B is a system diagram illustrating an example set of interfaces for an AR anchor implementation according to some embodiments.

[0091] FIG. 4 is a process diagram illustrating an example single AR anchoring in a gITF-based scene graph according to some embodiments.

[0092] FIG. 5 is a process diagram illustrating an example multiple AR anchoring in a gITF-based scene graph according to some embodiments.

[0093] FIG. 6A is a system diagram illustrating an example set of interfaces for a STAR-based 5G architecture, according to some embodiments.

[0094] FIG. 6B is a system diagram illustrating an example set of interfaces for an EDGAR-based 5G architecture, according to some embodiments.

[0095] FIG. 6C is a continuation of the system diagram of FIG. 6B illustrating an example set of interfaces for an EDGAR-based 5G architecture, according to some embodiments.

[0096] FIG. 7 is a message sequencing diagram illustrating an example process for delivering an AR experience, according to some embodiments.

[0097] FIG. 8 is a message sequencing diagram illustrating an example XR spatial compute pipeline, according to some embodiments.

[0098] FIG. 9 is a system diagram illustrating an example set of AR / MR QoE metrics observation points, according to some embodiments.

[0099] FIG. 10 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments.

[0100] FIG. 11 is a message sequencing diagram illustrating an example process for anchor creation delay measurement for remote spatial computing according to some embodiments.

[0101] FIG. 12 is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for STAR UE with local spatial computing according to some embodiments.

[0102] FIG. 13A is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for STAR UE with remote spatial computing according to some embodiments.

[0103] FIG. 13B is a continuation of the message sequencing diagram of FIG. 13A illustrating an example process for anchor detection to render to photon measurement for STAR UE with remote spatial computing according to some embodiments.

[0104] FIG. 14A is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for EDGAR UE with remote spatial computing and rendering according to some embodiments.

[0105] FIG. 14B is a continuation of the message sequencing diagram of FIG. 14A illustrating an example process for anchor detection to render to photon measurement for EDGAR UE with remote spatial computing and rendering according to some embodiments.

[0106] FIG. 15 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments.

[0107] FIG. 16 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments.

[0108] FIG. 17 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments.

[0109] FIG. 18 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments.

[0110] The entities, connections, arrangements, and the like that are depicted in— and described in connection with— the various figures are presented by way of example and not by way of limitation. As such, any and all statements or other indications as to what a particular figure "depicts,” what a particular element or entity in a particular figure "is” or "has,” and any and all similar statements— that may in isolation and out of context be read as absolute and therefore limiting— may only properly be read as being constructively preceded by a clause such as "In at least one embodiment, ... " For brevity and clarity of presentation, this implied leading clause is not repeated ad nauseum in the detailed description.DETAILED DESCRIPTION

[0111] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0112] As shown in FIG. 1 A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a ON 106, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a "station” and / or a "STA”, may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fl device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a UE.

[0113] The communications systems 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106, the Internet 110, and / or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0114] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0115] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0116] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 116 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0117] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0118] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0119] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., a eNB and a gNB).

[0120] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0121] The base station 114b in FIG. 1A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106.

[0122] The RAN 104 / 113 may be in communication with the CN 106, which may be any type of network configured to provide voice, data, applications, and / or voice over internet protocol (VoIP) services to one ormore of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing a NR radio technology, the CN 106 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0123] The CN 106 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and / or the internet protocol (IP) in the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communications networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0124] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0125] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0126] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0127] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0128] Although the transmit / receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0129] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11 , for example.

[0130] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, theprocessor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0131] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium- ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0132] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and / or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable locationdetermination method while remaining consistent with an embodiment.

[0133] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and / or Augmented Reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0134] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink(e.g. , for reception) may be concurrent and / or simultaneous. The full duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0135] Although the WTRU is described in FIGs. 1A-1 B as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0136] In representative embodiments, the other network 112 may be a WLAN.

[0137] In view of FIGs. 1A-1 B, and the corresponding description, one or more, or all, of the functions described herein may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0138] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and / or may performing testing using over-the-air wireless communications.

[0139] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0140] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments. An extended reality display device, together with its control electronics, may be implemented using a system such as the system of FIG. 1 D. System 150 can be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this document. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 150, singly or in combination, can be embodied in a single integrated circuit (IC), multiple ICs, and / or discrete components. For example, in at least one embodiment, the processing and encoder / decoder elements of system 150 are distributed across multiple ICs and / or discrete components. In various embodiments, the system 150 is communicatively coupled to one or more other systems, or other electronic devices, via, for example, a communications bus or through dedicated input and / or output ports. In various embodiments, the system 1000 is configured to implement one or more of the aspects described in this document.

[0141] The system 150 includes at least one processor 152 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document. Processor 152 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 150 includes at least one memory 154 (e.g., a volatile memory device, and / or a non-volatile memory device). System 150 may include a storage device 158, which can include non-volatile memory and / or volatile memory, including, but not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), flash, magnetic disk drive, and / or optical disk drive. The storage device 158 can include an internal storage device, an attached storage device (including detachable and non-detachable storage devices), and / or a network accessible storage device, as non-limiting examples.

[0142] System 150 includes an encoder / decoder module 156 configured, for example, to process data to provide an encoded video or decoded video, and the encoder / decoder module 156 can include its own processor and memory. The encoder / decoder module 156 represents module(s) that can be included in a device to perform the encoding and / or decoding functions. As is known, a device can include one or both of the encoding and decoding modules. Additionally, encoder / decoder module 156 can be implemented as a separate element of system 150 or can be incorporated within processor 152 as a combination of hardware and software as known to those skilled in the art.

[0143] Program code to be loaded onto processor 152 or encoder / decoder 156 to perform the various aspects described in this document can be stored in storage device 158 and subsequently loaded onto memory 154 for execution by processor 152. In accordance with various embodiments, one or more of processor 152, memory 154, storage device 158, and encoder / decoder module 156 can store one or more of various items during the performance of the processes described in this document. Such stored items can include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

[0144] In some embodiments, memory inside of the processor 152 and / or the encoder / decoder module 156 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device can be either the processor 152 or the encoder / decoder module 152) is used for one or more of these functions. The external memory can be the memory 154 and / or the storage device 158, for example, a dynamic volatile memory and / or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of, for example, a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2 (MPEG refers to the Moving Picture Experts Group, MPEG-2 is also referred to as ISO / IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC refers to High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Versatile Video Coding, a new standard being developed by JVET, the Joint Video Experts Team).

[0145] The input to the elements of system 150 can be provided through various input devices as indicated in block 172. Such input devices include, but are not limited to, (i) a radio frequency (RF) portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Component (COMP) input terminal (or a set of COMP input terminals), (iii) a Universal Serial Bus (USB) input terminal, and / or (iv) a High Definition Multimedia Interface (HDMI) input terminal. Other examples, not shown in FIG. 1 C, include composite video.

[0146] In various embodiments, the input devices of block 172 have associated respective input processing elements as known in the art. For example, the RF portion can be associated with elements suitable for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) downconverting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel incertain embodiments, (iv) demodulating the downconverted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion can include a tuner that performs various of these functions, including, for example, downconverting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, downconverting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and / or add other elements performing similar or different functions. Adding elements can include inserting elements in between existing elements, such as, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.

[0147] Additionally, the USB and / or HDMI terminals can include respective interface processors for connecting system 150 to other electronic devices across USB and / or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or within processor 152 as necessary. Similarly, aspects of USB or HDMI interface processing can be implemented within separate interface ICs or within processor 152 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 152, and encoder / decoder 156 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.

[0148] Various elements of system 150 can be provided within an integrated housing, Within the integrated housing, the various elements can be interconnected and transmit data therebetween using suitable connection arrangement 174, for example, an internal bus as known in the art, including the Inter- IC (I2C) bus, wiring, and printed circuit boards.

[0149] The system 150 includes communication interface 160 that enables communication with other devices via communication channel 162. The communication interface 160 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 162. The communication interface 160 can include, but is not limited to, a modem or network card and the communication channel 162 can be implemented, for example, within a wired and / or a wireless medium.

[0150] Data is streamed, or otherwise provided, to the system 150, in various embodiments, using a wireless network such as a Wi-Fi network, for example IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers). The Wi-Fi signal of these embodiments is received over the communications channel 162 and the communications interface 160 which are adapted for Wi-Fi communications. The communications channel 162 of these embodiments is typically connected to an access point or router that provides access to external networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 150 using a set-top box that delivers the data over the HDMI connection of the input block 172. Still other embodiments provide streamed data to the system 150 using the RF connection of the input block 172. As indicated above, various embodiments provide data in a non-streaming manner. Additionally, various embodiments use wireless networks other than Wi-Fi, for example a cellular network or a Bluetooth network.

[0151] The system 150 can provide an output signal to various output devices, including a display 176, speakers 178, and other peripheral devices 180. The display 176 of various embodiments includes one or more of, for example, a touchscreen display, an organic light-emitting diode (OLED) display, a curved display, and / or a foldable display. The display 176 can be for a television, a tablet, a laptop, a cell phone (mobile phone), or other device. The display 176 can also be integrated with other components (for example, as in a smart phone), or separate (for example, an external monitor for a laptop). The other peripheral devices 180 include, in various examples of embodiments, one or more of a stand-alone digital video disc (or digital versatile disc) (DVR, for both terms), a disk player, a stereo system, and / or a lighting system. Various embodiments use one or more peripheral devices 180 that provide a function based on the output of the system 150. For example, a disk player performs the function of playing the output of the system 150.

[0152] In various embodiments, control signals are communicated between the system 150 and the display 176, speakers 178, or other peripheral devices 180 using signaling such as AV.Link, Consumer Electronics Control (CEC), or other communications protocols that enable device-to-device control with or without user intervention. The output devices can be communicatively coupled to system 1000 via dedicated connections through respective interfaces 164, 166, and 168. Alternatively, the output devices can be connected to system 150 using the communications channel 162 via the communications interface 160. The display 176 and speakers 178 can be integrated in a single unit with the other components of system 150 in an electronic device such as, for example, a television. In various embodiments, the display interface 164 includes a display driver, such as, for example, a timing controller (T Con) chip.

[0153] The display 176 and speaker 178 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 172 is part of a separate set-top box. In variousembodiments in which the display 176 and speakers 178 are external components, the output signal can be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

[0154] The system 150 may include one or more sensor devices 168. Examples of sensor devices that may be used include one or more GPS sensors, gyroscopic sensors, accelerometers, light sensors, cameras, depth cameras, microphones, and / or magnetometers. Such sensors may be used to determine information such as user's position and orientation. Where the system 150 is used as the control module for an extended reality display (such as control modules 124, 132), the user's position and orientation may be used in determining how to render image data such that the user perceives the correct portion of a virtual object or virtual scene from the correct point of view. In the case of head-mounted display devices, the position and orientation of the device itself may be used to determine the position and orientation of the user for the purpose of rendering virtual content. In the case of other display devices, such as a phone, a tablet, a computer monitor, or a television, other inputs may be used to determine the position and orientation of the user for the purpose of rendering content. For example, a user may select and / or adjust a desired viewpoint and / or viewing direction with the use of a touch screen, keypad or keyboard, trackball, joystick, or other input. Where the display device has sensors such as accelerometers and / or gyroscopes, the viewpoint and orientation used for the purpose of rendering content may be selected and / or adjusted based on motion of the display device.

[0155] The embodiments can be carried out by computer software implemented by the processor 152 or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The memory 154 can be of any type appropriate to the technical environment and can be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples. The processor 152 can be of any type appropriate to the technical environment, and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples.Block-Based Video Coding

[0156] Like HEVC, the VVC is built upon the block-based hybrid video coding framework. FIG. 2A gives the block diagram of a block-based hybrid video encoding system 200. Variations of this encoder 200 are contemplated, but the encoder 200 is described below for purposes of clarity without describing all expected variations.

[0157] Before being encoded, a video sequence may go through pre-encoding processing (204), for example, applying a color transform to an input color picture (e.g., conversion from RGB 4:4:4 to YCbCr4:2:0), or performing a remapping of the input picture components in order to get a signal distribution more resilient to compression (for instance using a histogram equalization of one of the color components). Metadata can be associated with the pre-processing and attached to the bitstream.

[0158] The input video signal 202 including a picture to be encoded is partitioned (206) and processed block by block in units of, for example, CUs. Different CUs may have different sizes. In VTM-1 .0, a CU can be up to 128x128 pixels. However, different from the HEVC which partitions blocks only based on quadtrees, in the VTM-1 .0, a coding tree unit (CTU) is split into CUs to adapt to varying local characteristics based on quad / binary / ternary-tree. Additionally, the concept of multiple partition unit type in the HEVC is removed, such that the separation of CU, prediction unit (PU) and transform unit (TU) does not exist in the VVC-1.0 anymore; instead, each CU is always used as the basic unit for both prediction and transform without further partitions. In the multi-type tree structure, a CTU is firstly partitioned by a quad-tree structure. Then, each quad-tree leaf node can be further partitioned by a binary and ternary tree structure. Different splitting types may be used, such as quaternary partitioning, vertical binary partitioning, horizontal binary partitioning, vertical ternary partitioning, and horizontal ternary partitioning.

[0159] In the encoder of FIG. 2A, spatial prediction (208) and / or temporal prediction (210) may be performed. Spatial prediction (or "intra prediction”) uses pixels from the samples of already coded neighboring blocks (which are called reference samples) in the same video picture / slice to predict the current video block. Spatial prediction reduces spatial redundancy inherent in the video signal. Temporal prediction (also referred to as "inter prediction” or "motion compensated prediction”) uses reconstructed pixels from the already coded video pictures to predict the current video block. Temporal prediction reduces temporal redundancy inherent in the video signal. A temporal prediction signal for a given CU may be signaled by one or more motion vectors (MVs) which indicate the amount and the direction of motion between the current CU and its temporal reference. Also, if multiple reference pictures are supported, a reference picture index may additionally be sent, which is used to identify from which reference picture in the reference picture store (212) the temporal prediction signal comes.

[0160] The mode decision block (214) in the encoder chooses the best prediction mode, for example based on a rate-distortion optimization method. This selection may be made after spatial and / or temporal prediction is performed. The intra / inter decision may be indicated by, for example, a prediction mode flag. The prediction block is subtracted from the current video block (216) to generate a prediction residual. The prediction residual is de-correlated using transform (218) and quantized (220). (For some blocks, the encoder may bypass both transform and quantization, in which case the residual may be coded directly without the application of the transform or quantization processes.) The quantized residual coefficients are inversequantized (222) and inverse transformed (224) to form the reconstructed residual, which is then added back to the prediction block (226) to form the reconstructed signal of the CU. Further in-loop filtering, such as deblocking / SAO (Sample Adaptive Offset) filtering, may be applied (228) on the reconstructed CU to reduce encoding artifacts before it is put in the reference picture store (212) and used to code future video blocks. To form the output video bit-stream 230, coding mode (inter or intra), prediction mode information, motion information, and quantized residual coefficients are all sent to the entropy coding unit (108) to be further compressed and packed to form the bit-stream.

[0161] FIG. 2B gives a block diagram of a block-based video decoder 250. In the decoder 250, a bitstream is decoded by the decoder elements as described below. Video decoder 250 generally performs a decoding pass reciprocal to the encoding pass as described in FIG. 2A. The encoder 200 also generally performs video decoding as part of encoding video data.

[0162] In particular, the input of the decoder includes a video bitstream 252, which can be generated by video encoder 200. The video bit-stream 252 is first unpacked and entropy decoded at entropy decoding unit 254 to obtain transform coefficients, motion vectors, and other coded information. Picture partition information indicates how the picture is partitioned. The decoder may therefore divide (256) the picture according to the decoded picture partitioning information. The coding mode and prediction information are sent to either the spatial prediction unit 258 (if intra coded) or the temporal prediction unit 260 (if inter coded) to form the prediction block. The residual transform coefficients are sent to inverse quantization unit 262 and inverse transform unit 264 to reconstruct the residual block. The prediction block and the residual block are then added together at 266 to generate the reconstructed block. The reconstructed block may further go through in-loop filtering 268 before it is stored in reference picture store 270 for use in predicting future video blocks.

[0163] The decoded picture 272 may further go through post-decoding processing (274), for example, an inverse color transform (e.g. conversion from YCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverse of the remapping process performed in the pre-encoding processing (204). The post-decoding processing can use metadata derived in the pre-encoding processing and signaled in the bitstream. The decoded, processed video may be sent to a display device 276. The display device 276 may be a separate device from the decoder 250, or the decoder 250 and the display device 276 may be components of the same device.

[0164] Various methods and other aspects described in this disclosure can be used to modify modules of a video encoder 200 or decoder 250. Moreover, the systems and methods disclosed herein are not limited to WC or HEVC, and can be applied, for example, to other standards and recommendations, whether preexisting or future-developed, and extensions of any such standards and recommendations (including WCand HEVC). Unless indicated otherwise, or technically precluded, the aspects described in this disclosure can be used individually or in combination.

[0165] The application provides a framework for configuring and measuring Quality of Experience (QoE) metrics for each Augmented Reality (AR) anchor of a scene. These QoE metrics depend on the type of trackable element (on the model of an element of the real world of which features are available and / or may be extracted) and on the User Equipment (UE) architecture (e.g., a standalone and Edge-based UE architecture). Based on these measurements, an XR application may perform adjustments to improve the AR anchoring process.

[0166] The framework may include retrieval of AR anchoring information used for the AR experience, and for each AR anchor, may include performing the following tasks: configuration of QoE metrics; measurement of anchor creation QoE metrics; measurement of anchor detection QoE metrics; measurement of anchor tracking QoE metrics; and / or adjustment of the AR anchoring process after each measurement.

[0167] For each QoE metric related to delay / latency, pieces of the timing information may be recorded in a dedicated-metric QoE metadata structure which is exchanged between the UE and a server. At each functional block, the UE and the server may record timing information to a dedicated-metric QoE metadata structure. This structure may be persistent over the whole processing pipeline. For this application, a trackable object may be referred to as a "trackable”. Additionally, an anchor object may be referred to as an "anchor”. In addition to the framework mentioned above, four new QoE metrics for AR anchoring are created: Anchor Creation Delay (ACD) metric, Anchor Detection-to-Render-to-Photon (ADRP) latency metric, Anchor Untracked Ratio (AUR) metric, and Trackable Pose Prediction Error (TPPE) spatial metric.

[0168] The Anchor Creation Delay (ACD) metric corresponds to the delay between the time of the anchor creation request and the time when the related XR space (the frame of reference in which the 3D coordinates are expressed) is created.

[0169] The Anchor Detection-to-Render-to-Photon (ADRP) latency metric corresponds to the delay between the time of the anchor pose request leading to the detection of the trackable and the time when the virtual content is displayed in the user's real environment.

[0170] The Anchor Untracked Ratio (AUR) metric corresponds to the ratio between the number of frames where the trackable associated with the anchor is not tracked and the total number of frames during the observation period in which no detection process is launched.

[0171] The Trackable Pose Prediction Error (TPPE) spatial metric corresponds to the difference between the predicted pose (position and orientation) of the trackable calculated in the spatial computing function and the pose of that trackable in the user's real environment.User Real Environment and AR Anchoring

[0172] FIG. 3A is a schematic illustration showing an example AR anchoring according to some embodiments. FIG. 3B is a system diagram illustrating an example set of interfaces for an AR anchor implementation according to some embodiments. FIG. 3A shows a real world environment 300, while FIG. 3B shows an associated world graph 350.

[0173] In Augmented Reality (AR) experiences, virtual content may be inserted into a user real environment using optical or video-see through XR devices. Knowledge of a user's real environment may be obtained from sensor data using XR spatial computing functions such as Simultaneous Localization And Mapping (SLAM) for spatial mapping, such as creating a map of a surrounding area, and localization, such as establishing the pose of users and objects within that space, 3D reconstruction and semantic perception.

[0174] To establish a pose of virtual objects in a user's real environment, the concept of AR anchoring has been defined based on trackable and anchor entities. A trackable is a model of an element of the real world for which features are available and / or may be extracted. Each trackable provides a local reference space in which an anchor pose may be expressed. An anchor corresponds to a real-world pose, identified using one or more trackables. Each anchor provides a local reference space in which a pose may be expressed. This local reference space is derived from the trackable local reference space using a pre-defined 3D transformation.

[0175] Trackable and anchor entities are mentioned in the requirements document, ETSI Group Specification (GS) Augmented Reality Framework (ARF) Interoperability Requirements for AR Components, Systems, and Services Part 2: World Storage and AR Authoring Functions, ETSI, v. 1.1.1 (August 2021) ("ETS! GS ARF 004-2’). A typical example of AR anchor implementation within ETSI ARF is shown in FIGs. 3A and 3B.

[0176] Each anchor 302, 304, 352, 354 (called a world anchor” in FIG. 3B) is linked to each trackable 306, 308, 310, 356, 358, 360 to be used for computing the pose of the associated world anchor in the real world. The first AR asset 312, 362 (the gauge of FIGs. 3A and 3B) may be triggered only if trackable T-0X (306, 356) is detected. The second AR asset 314, 364 (the word "Online”) may be triggered by any of the 3 trackables 306, 308, 310, 356, 358, 360 (IDs: T-0X, T-0Y, and T-0Z) or a combination of them.

[0177] This hierarchy of trackables and world anchors may be expressed with a world graph and represents real world knowledge in the world representation sub-function. FIG. 3A shows what these AR assets may look like in the real world, while FIG. 3B shows a world graph expressing spatial relationships between, for example, trackables, anchors, and AR assets.AR Anchoring in MPEG-I Scene Description

[0178] FIG. 4 is a process diagram illustrating an example single AR anchoring in a gITF-based scene graph according to some embodiments. FIG. 5 is a process diagram illustrating an example multiple AR anchoring in a gITF-based scene graph according to some embodiments. FIGs. 4 and 5 shows example gITF-based processes 400, 500.

[0179] The MPEG-I Scene Description specification currently specifies support for AR anchoring in a Khronos gITF-based scene description file. See the gITF Specification, version 2.0, KHRONOS 3D FORMATS WORKING GROUP (2021) (“gITF Specification"). The input contribution, Hirtzlin, Patrice, et al., [SD] EE7 - AR Anchoring in Scene Graph, ISO / IEC JTC 1 / SC 29 / WG 3 m59958 (July 2022) ("AR Anchoring in Scene Graph"), proposed a method to support trackable objects and anchor objects within a gITF scene graph. Virtual assets take benefit from inherent spatial relationships between the graph nodes as illustrated in FIGs. 4 (single AR anchoring for a gITF-based scene) and 5 (multiple AR anchoring for a gITF-based scene).

[0180] While the MPEG-I Scene Description specification takes the perspective that an anchor is a root node, such a situation may occur in which an anchor is not a root node for some embodiments outside an MPEG-I Scene Description environment. In such a situation, the "parent” node is not considered for spatial positioning once a related trackable is detected.

[0181] Based on that AR Anchoring in Scene Graph contribution, MPEG-I Scene Description has specified support for AR anchoring in section 8.1 of ISO / IEC JTC1 / SC29 / WG03 N00797, Text of ISO / IEC 23090-14 CDAM 2: Support for Haptics, Augmented Reality, Avatars, Interactivity, MPEG-I Audio, and Lighting, June 20, 2023 (“MPEG-I Scene Description CDAM2'). Table 1 describes details of an anchor object. See Table 8.1-2 of MPEG-I Scene Description CDAM2.Table 1

[0182] Table 2 describes details of a trackable object. See Table 8.1-3 of MPEG-I Scene Description CDAM2 .System Architecture

[0183] FIG. 6A is a system diagram illustrating an example set of interfaces for a STAR-based 5G architecture according to some embodiments. FIG. 6B is a system diagram illustrating an example set of interfaces for an EDGAR-based 5G architecture according to some embodiments. FIG. 6C is a continuation of the system diagram of FIG. 6B illustrating an example set of interfaces for an EDGAR-based 5G architecture, according to some embodiments.

[0184] The 3GPP TSG SA WG4 (SA4) has studied different architectures for enabling XR experiences. Different degrees of split workflow between the AR devices and the Cloud / Edge are defined in the specification Support of 5G Glass-Type Augmented Reality / Mixed Reality (AR / MR) Devices, 3GPP, TR 26.998, version 17.1 .0 (“TR 26.998"). The following two main device types have been defined: 5G Standalone AR UE (STAR) and 5G Edge dependent AR UE (EDGAR). For a 5G Standalone AR (STAR) User Equipment (UE) environment 600, which is shown in FIG. 6A, most of the XR functions are performed locally on the UE. For a 5G Edge Dependent AR (EDGAR) UE environment 650, which is shown in FIGs. 6B-6C, most of the XR functions, especially the rendering, are performed on a Cloud / Edge device

[0185] With respect to the AR anchoring, the following functional blocks are particularly involved: the AR runtime 602, 652, the AR scene manager 604, the media access functions 606, 656 and the media delivery functions 658.

[0186] The AR Runtime 602, 652 is composed of a set of functions, such as functions related to accessing controller / peripheral state, getting current and / or predicted user pose, performing XR spatial computing, and submitting rendered frames to the display. More precisely, the detection and the tracking of a trackable is performed in the XR spatial computing function to compute the pose of the related local reference space.

[0187] The AR Scene Manager 604 is composed of a set of functions for maintaining and rendering an up-to-date XR scene represented by a scene graph. More precisely, the type of trackable to be detected and the spatial relationships between the trackable, the anchor, and the virtual assets to be anchored are defined and maintained in the scene graph handler function.

[0188] The Media Access Functions 606, 656 and Media Delivery Functions 658 enable access to media and other related AR related data using 5G system functionalities. More precisely, these functions are used for AR anchoring to transmit / receive XR data when the XR spatial computing function is performed on the Edge device(s).

[0189] Based on these architectures, the following use cases may be considered for support of AR anchoring: when all the required functionalities are in the UE (STAR UE), when the XR spatial computing is performed on the Edge (STAR UE), and when the XR spatial computing and the rendering are performed on the Edge (EDGAR UE)Delivering an AR Experience

[0190] FIG. 7 is a message sequencing diagram illustrating an example process for delivering an AR experience according to some embodiments. A workflow to deliver an AR experience is identified in the specification TR 26.998 in line with the architecture shown in FIGs. 6A, 6B, and 6C.

[0191] FIG. 7 provides a typical workflow 700 for setting up a session between a 5G UE and a network / cloud for receiving AR scenes from a scene provider. In some embodiments, an AR / MR application 702 fetches 718 content from an AR / MR application provider 716.

[0192] The AR / MR application 702 may initialize 720 a scene manager 706 with a URL / location of a scene description. The type of trackable and the spatial relationships between the trackable, the anchor, and the virtual assets to be anchored are provided in the input scene description document, which are shown as steps 2b (722) and 2c (724) of FIG. 7. For example, the scene manager 706 may retrieve 722 a scenedescription from a scene provider 712, and the scene manager 706 may parse 724 and process the scene description. The scene manager 706 may establish 726 an AR / MR session with an AR Runtime block 704. The AR Runtime block 704 may create 728 and initialize an AR / MR session.

[0193] For some embodiments, an AR media delivery pipeline 730 may be created between two or more of the following blocks: the AR Runtime block 704, the scene manager 706, the media access function 708, the media delivery function 710, and the scene provider 712.

[0194] How to perform detection and tracking of the trackable(s) is negotiated in a dedicated XR spatial computing pipeline, step 4 (732). Step 4 of FIG. 7 is shown in more detail as FIG. 8 based on the UE architecture type, the type of trackable, and the capabilities of the UE. For some embodiments, an XR spatial compute pipeline 732 may be created between two or more of the following blocks: the AR Runtime block 704, the scene manager 706, the media access function 708, the media delivery function 710, the scene provider 712, and the XR spatial compute server 714.

[0195] In step 5 (734) of FIG. 7, the computed pose(s) of the trackable(s) is / are then used by the scene manager 706 to maintain the spatial consistency of the AR scene represented by a scene graph. For some embodiments, one or more of the following blocks may combine 734 AR scene data and XR spatial compute data: the AR / MR application 702, the AR runtime 704, and the scene manager 706.

[0196] FIG. 8 is a message sequencing diagram illustrating an example XR spatial compute pipeline according to some embodiments. In some embodiments of an example XR spatial compute pipeline 800, the AR runtime block 804 initializes 818 XR spatial compute downlink delivery via the media access function 808. The media access function 808 may request 820 QoS and resources for downlink from the media delivery function 810. The AR runtime 804 may request 822 XR spatial description information from the XR spatial compute server 814. The XR spatial compute server 814 may establish 824 an XR spatial description downlink delivery session with the AR runtime block 804. The XR spatial compute server 814 may deliver 826 XR spatial description information to the AR runtime block 804.

[0197] The AR runtime block 804 may initialize 828 XR spatial compute uplink delivery with the media access function 808. The media access function 808 may request 830 QoS and resources for uplink with the media delivery function 810. The media access function 808 may establish 832 an uplink delivery pipeline with the media delivery function 810. The AR runtime block 804, the media access function 808, the media delivery function 810, and the XR spatial compute server 814 may establish 834 an XR spatial compute uplink delivery session. The AR runtime block 804, the media access function 808, the media delivery function 810, and the XR spatial compute server 814 may communicate 836 upstream XR spatial compute information. The AR runtime block 804 and the scene manager 806 may exchange 838 data.Khronos OpenXR

[0198] The specification, The OpenXR Specification, Khronos Group (2022) (“OpenXR Specification"), provides a cross-platform access directly into diverse XR device runtimes across multiple platforms. OpenXR enables applications and engines to run on any system that exposes the OpenXR API. This API corresponds to the AR / XR runtime interface shown in FIGs. 6A, 6B, 6C, and 9.

[0199] For example, this open API may be used by an XR application: (1) to handle / get the results of the XR runtime functions such as the status of user / controller actions, the current / predicted pose of the user gesture, and the pose of the trackable from the spatial computing function, and (2) to submit rendered frames to the display. More precisely, an XR application may rely on the following OpenXR API functions to handle the AR anchoring process.

[0200] The XR runtime may be configured with the type of trackable to detect and track. The function xrCreateReferenceSpace with the xrReferenceSpaceType parameter may be used to specify the type of trackable to consider. For example, the STAGE reference space considers the floor as a trackable. The function xrCreateActionSpace may be used to define a trackable related to a user body part (e.g., a user's right hand) and an action / gesture (e.g., click, grip, or aim). The functions xrCreateSpatialAnchorFB and xrCreateSpatialAnchorMSFT may be used to define a trackable related to an arbitrary freespace point in a user's real environment. A previously stored spatial anchor may be re-used using the API function xrQuerySpacesFB with xrCreateSpatialAnchorFromPersistedNameMSFT

[0201] A check may be done to see if the requested type of trackable is supported by the UE. For example, the return value of the function xrCreate() may be checked. A return value of XR_SUCCESS is returned if the trackable is supported. A return value of XR_ERROR_REFERENCE_SPACE_UNSUPPORTED may be returned by the function xrCreateReferenceSpace if the trackable is not supported. Similarly, a return value of XR_ERROR_CREATE_SPATIAL_ANCHOR_FAILED_MSFT may be returned by the function xrCreateSpatialAnchorMSFT if the trackable is not supported.

[0202] The status and pose of a trackable may be retrieved. The function xrLocateSpace with the xrSpaceLocationFlags parameters may be used to indicate if the pose (position and orientation) of the trackable is valid and / or still tracked at a particular point in time.

[0203] The predicted display time at which the next frame will be displayed to the user may be retrieved. The function xrWaitframe returns the predicted display time for the xrFrameState structure that is passed as a parameter.XR Spatial Computing QoE Requirements and Metrics

[0204] FIG. 9 is a system diagram illustrating an example set of AR / MR QoE metrics observation points according to some embodiments. Contrary to some other XR QoE requirements, such as the motion-to- photon or user interaction delay, no specific QoE requirements are known to have been yet published for the XR spatial computing function.

[0205] The following QoE metrics involving the spatial computing function have been defined in the section 6.3 of the specification Study on QoE Metrics for AR / MR Services, 3GPP, TR 26.812, Release 18, version 0.5.0 ("TR 26.812') the scene startup latency metric, the interaction latency metric, and the tracking position prediction error metric.

[0206] Scene startup latency indicates the time from the application is started until the remote initial AR scene is displayed in the right place of the reconstructed 3D space. For instance, once the AR application is started, an initial AR scene is requested by the client and sent back to the AR runtime.

[0207] The interaction latency indicates the time from when the new AR scene is requested until the remote new AR scene is displayed. For example, if a user clicks to request a specific AR object to appear in front, the AR scene is requested by the client and sent back to the AR runtime for rendering and display.

[0208] Tracking position prediction error mainly refers to the relative position error, which indicates the deviation between the relative position in the real world and the predicted position. This error may be observed at observation point OP-1 (902) of FIG. 9 and derived by comparing the predicated space locations and real space locations. The actual location may not be known in an XR session. The example environment 900 shows other example observations points OP-2 (904), OP-3 (906), and OP-4 (908).Problem Statement

[0209] The two QoE metrics on latency introduced in the TR 26.812 document are believed to have the following limitations. There is no indication of how an XR application concretely retrieves the exact timing information to calculate these latency metrics.

[0210] There is only consideration of an overall latency between the application start or a user event and the final rendering of the virtual scene. There is a need to decompose these overall latencies into elementary delays to determine where the time is spent (e.g., at the scene graph handler, at the creation / loading of a spatial anchor, in the spatial computing function, in the network for the remote spatial computing case) for potential adjustments (e.g., displaying a warning message telling the user to move to a more favorable location).

[0211] There is only consideration of a single trackable and anchor for the whole virtual scene, but a scene description file may define several trackables and related anchors for a single scene. There is a need of defining QoE metrics on a per anchor basis as the AR anchoring tracking performance and / or delay may depend on the type of trackable. Hence, some parts of the virtual scene may be displayed at the right place before other parts of the virtual scene which are attached to other anchors.

[0212] There is only consideration of the position for the spatial error metric. The orientation is also required to compute a spatial error based on the predicted pose.

[0213] There is only consideration of latency and spatial error. Other QoE metrics may be relevant to characterize the tracking performance (e.g., the ratio between the number of frames where the trackable associated with the anchor is not detected to the total number of frames during the observation period).

[0214] FIG. 10 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments. For some embodiments, the process 1000 shown in FIG. 10 may be executed within the scope of the AR Runtime API of the OpenXR Specification. For some embodiments, the process 1000 shown in FIG. 10 may be executed within the scope of an MPEG-I Scene Description with the AR anchoring extension provided in section 8.1 of the MPEG-I Scene Description CDAM2 specification. This extension describes different types of trackables that may be retrieved for use by an XR experience delineated in a scene description file.

[0215] As shown in FIG. 10, the framework may include retrieval 1002 of AR anchoring information used for the AR experience, and for each AR anchor, may include performing the following tasks: configuration 1004 of QoE metrics; measurement 1006 of anchor creation QoE metrics; measurement 1008 of anchor detection QoE metrics; measurement 1010 of anchor tracking QoE metrics; and / or adjustment 1012 of the AR anchoring process after each measurement. These tasks are described below with regard to QoE metrics for AR anchoring. Although FIG. 10 shows arrows going in and out of the Adjust AR Anchor Process box after each metric measurement, such an Adjust AR Anchor Process may or may not run after each metric for some embodiments. For example, the Adjust AR Anchor Process may run after measuring a creationbased QoE metric the first time such a metric is measured, but the Adjust AR Anchor Process may not run the next time the creation-based QoE metric is measured for another Anchor. Similarly, for example, the Adjust AR Anchor Process may run after measuring a detection-based QoE metric, but the Adjust AR Anchor Process may not run after measuring a tracking-based QoE metric. Also, for some embodiments, if no adjustments via the Adjust AR Anchor Process are dictated, the Adjust AR Anchor Process may not run or may run but may not make any adjustments. Additionally, for some embodiments, more or less metrics may be measured than what are shown in FIG. 10.

[0216] For some embodiments, a determination 1014 may be performed to determine if trackable detection is required. If trackable detection is required, then the process 1000 returns to measure 1008 detection-based QoE metrics. Otherwise, the process 1000 proceeds to measure 1010 tracking-based QoE metrics.Definition of QoE Metrics for AR Anchoring

[0217] Four QoE metrics are believed to offer more accurate measurement and control of the AR anchoring process for each trackable of an AR scene compared to the two previous QoE latency metrics of the spatial computing function defined in section 6.3 of the TR 26.812 document.

[0218] The Anchor Creation Delay (ACD) metric corresponds to the delay between the time of the anchor creation request and the time when the related XR space (the frame of reference in which the 3D coordinates are expressed) is created.

[0219] The Anchor Detection-to-Render-to-Photon (ADRP) latency metric corresponds to the delay between the time of the anchor pose request leading to the detection of the trackable and the time when the virtual content is displayed in the user's real environment.

[0220] The Anchor Untracked Ratio (AUR) metric corresponds to the ratio between the number of frames where the trackable associated with the anchor is not tracked and the total number of frames during the observation period in which no detection process is launched.

[0221] The Trackable Pose Prediction Error (TPPE) spatial metric corresponds to the difference between the predicted pose (position and orientation) of the trackable calculated in the spatial computing function and the pose of that trackable in the user's real environment.

[0222] The two previous QoE latency metrics (interaction latency and scene startup latency) of the spatial computing function defined in section 6.3.2 of the TR 26.812 document may be derived from these newly defined metrics.

[0223] The interaction latency corresponds to the ADRP latency metric for the first detection of the trackable. After a first detection, the spatial computing function may predict the pose of the trackable, even if the trackable is no longer tracked, thereby enabling the anchoring, positioning, and display of virtual content (even if the tracking position error may increase). This interaction latency metric may not be relevant from an AR anchoring perspective and may be more related to the interactivity requirements and QoE (e.g., roundtrip interaction delay). After a first detection, the TPPE and AUR metrics may be more relevant from an AR anchoring perspective.

[0224] The initial scene start-up latency corresponds to the sum of the first initialization step delay (fetching content entry point, initialization of the scene manager, retrieval of the scene description file, which are shown as steps 1 , 2a, and 2b of FIG. 7), the ACD delay, the delay until the anchor is first detected, and the ADRP delay in the case of a single anchor for the whole scene.Retrieval of AR Anchoring Information

[0225] Once the scene description file is received by the UE, the scene manager parses the file and retrieves the AR anchoring information required for that AR experience (step 2c of FIG. 7).

[0226] As specified in the section 8.1 of MPEG-I Scene Description CDAM2, AR anchoring information includes: (1) the different types of trackable to be supported; (2) for each trackable, the spatial relationship between the trackable, its related anchor, and the virtual content to be anchored; and (3) some optional metadata specifying how to handle the AR anchoring process at runtime. For example, some optional metadata may indicate whether or not to display the virtual content at a default location until the trackable is detected, and may define a minimum available space in the user's real environment to allow anchoring of virtual content.

[0227] From a QoE metric perspective, the most relevant AR anchoring information is the type(s) of trackable(s) used for a AR experience since some types of trackables may not be supported locally by a UE. Such lack of support may lead to an Edge delegation for the spatial computing function for that trackable. This delegation may directly impact the configuration and measurement of latency / delay related QoE AR anchoring metrics, such as ACD and ADRP.Configuration of QoE Metrics

[0228] The configuration of the AR anchoring QoE metrics is related to the steps 2d, 2e, and 4 of FIG. 7 in which the UE and the Edge server negotiate and establish support of the spatial computing function. This configuration is done for each anchor defined in the scene description file. The configuration of the QoE metrics depends on the UE architecture (STAR or EDGAR architecture) and the type of trackable related to that anchor because such type of trackable may impact the location (in the UE or remotely in an Edge server) of the spatial computing function for that trackable

[0229] The end-to-end latency QoE metrics, such as ADRP, are typically the sum of elementary delays from the processing of functional blocks (e.g., network transmission, scene update, rendering) having identified interfaces, APIs, and / or observation points.

[0230] For each latency QoE metric, the configuration includes identification of: which elementary delays are relevant to measure and where to insert the timing information accordingly. For each latency QoE metric, the different pieces of timing information are recorded into a dedicated-metric QoE metadata structure, which is exchanged between the UE and the server. At each relevant functional block, the UE and the server record the related timing information to that dedicated-metric QoE metadata structure which is persistent over the whole processing pipeline.

[0231] The periodicity of measurement of tracking based QoE metrics, such as AUR and TPPE, also may be configurated depending on the relevance level of that metric for this anchor. For instance, this periodicity may be a multiple (N >= 1) of the rendering frame period. More specifically to the AUR metric, the observation period in which the ratio is calculated also may be configurated depending, for instance, on the time evolving / dynamic behavior of the AR experience. For instance, this observation period may be a large multiple (N >= 100) of the rendering frame period.Measurement of Anchor Creation QoE Metrics

[0232] These QoE metrics are typically related to the set-up / creation of an anchor such as the ACD metric. They may be measured once during the runtime. As an embodiment of an anchor creation QoE metric, the ACD metric corresponds to the delay between the time of the anchor creation request and the time when the related XR space (the frame of reference in which the 3D coordinates are expressed) is created. This metric is measured at the interface between the AR runtime and the scene manager if the type of trackable is supported locally in the UE. The ACD metric corresponds to the observation point OP-1 of FIG. 9.

[0233] As an example, the Khronos OpenXR API (described above) may be used to measure the ACD start and end times. The ACD start time corresponds to the time of the anchor creation request, which is when the xrCreateReferenceSpace, xrCreateActionSpace, xrCreateSpatialAnchorFB, xrCreateSpatialAnchorMSFT, or xrCreateSpatialAnchorFromPersistedNameMSFT function is called depending on the type of trackable supported. The ACD end time corresponds to the time when an XR_SUCCESS return value is received. The ACD metric for such an anchor is shown in Eq. 1 :ACD = End Time — Start Time (1)

[0234] In the case of remote spatial computing (which is when the type of trackable is not supported locally in the UE), the ACD metric may be measured by using a dedicated ACD QoE metadata structure. Table 3 identifies which functional block(s) may have a major contribution to the overall delay. The functional block(s) to be considered for such an anchor are defined in the configuration step.

[0235] Table 3 shows the ACD QoE metadata structure.Table 3

[0236] FIG. 11 is a message sequencing diagram illustrating an example process for anchor creation delay (ACD) measurement for remote spatial computing according to some embodiments. For some embodiments, the process 1100 for determining an ACD measurement may be as shown in FIG. 11. For item 1 (1108), the UE (which includes the XR runtime block 1102 and the scene graph handler 1104) and the Server (which includes the XR computing block 1106) configure the anchor creation QoE. A delegation to a XR Spatial Computing server is established for that anchor as the trackable cannot be supported by the UE. For item 2 (1110), the request of the anchor creation is sent by the UE Scene Graph Handler to the XR Spatial Computing Server. A unique anchor identifier (anchor-id) and the anchor-creation-request-time are appended to the ACD QoE metadata structure. For item 3 (1112), the anchor creation request is received by the XR server. The XR server appends the anchor-creation-start-time to the ACD QoE metadata structure when the XR server starts creation of the anchor. Once the XR Server has created the anchor, the XR server appends the anchor-creation-end-time to the ACD QoE metadata structure and transmits the acknowledgement to the UE as shown in item 4 (1114). For item 5 (1116), the scene manager receives the acknowledgment of the anchor creation and appends the anchor-creation-end-RX-time to the ACD QoE metadata structure.

[0237] Based on this ACD measurement call flow, the UE may measure the ACD 1118 for a particular anchor as shown in Eq. 2:ACD = anchor creation end RX time - anchor creation request time (2)

[0238] Depending on the granularity of the ACD delay for that anchor defined during the configuration step, the UE, for some embodiments, may measure the following network and server processing delays shown in Eqs. 3-6: network uplink delay = anchor creation start time — anchor creation request time (3) server anchor creation delay = anchor creation end time — anchor creation start time (4) network downlink delay = anchor creation end RX time — anchor creation end time (5) network roundtrip delay = network uplink delay + network downlink delay (6)

[0239] The WebRTC data channel may be used to send the ACD QoE metadata structure with the anchor creation request or response. Each of these pieces of delay information may be used to adjust the AR anchoring process before further spatial computing processing for that anchor on that server.Measurement of Anchor Detection Metric

[0240] Measurement of the anchor detection metric may occur each time a detection process of the trackable related to a particular anchor is launched. Typically, this step occurs at the beginning after anchor creation and after a potential first adjustment of the AR anchoring process; or when the trackable associated with an anchor was not visible and becomes visible again. As an embodiment of an anchor detection QoE metric, the ADRP latency metric corresponds to the delay between the time of the anchor pose request leading to the detection of the trackable and the time when the virtual content is displayed in the user's real environment.

[0241] As an example, the Khronos OpenXR API (described above) may be used to measure the ADRP start and end times. The ADRP start time corresponds to the time of the anchor pose request leading to the detection of the trackable. The xrLocateSpace() function may be used to request an anchor pose at a current or predicted time. The anchor detection time corresponds to the time when both the bitwise comparisons of (XR_SPACE_LOCATION_POSITION_VALID_BIT & XrSpaceLocationFlags) and (XR_SPACE_LOCATION_POSITION_ TRACKED _BIT & XrSpaceLocationFlags) are changed from unset to set when querying the xrLocateSpace() function. The ADRP end time corresponds to the predicted display time for that frame as returned by the xrWaitFrame.

[0242] In the case of remote spatial computing (which is when the type of trackable is not supported locally in the UE, the ADRP metric may be measured by using a dedicated ADRP QoE metadata structure. Table 4 identifies which functional block(s) may have a major contribution to the overall delay. The functional block(s) to be considered for such an anchor are defined in the configuration step.

[0243] Table 4 shows the ADRP QoE metadata structure.Table 4

[0244] FIG. 12 is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for STAR UE with local spatial computing according to some embodiments. For some embodiments, the process 1200 for determining an anchor detection to render to photon (ADRP) measurement 1230 for STAR UE with local spatial computing may be as shown in FIG. 12.

[0245] For item 1 (1212), the Scene Graph Handler 1210 requests the pose of the anchor to the XR Spatial Compute function 1206. The UE records the anchor-pose-request-time. For item 2 (1214), the XR Spatial Computing function 1206 detects the anchor. For item 3 (1216), the XR Spatial Computing function 1206 sends the anchor pose to the Scene Graph Handler 1210. The UE records the anchor-detection-time. For item 4 (1218), the UE updates the scene via the visual Tenderer 1208 by enabling the virtual assets related to that anchor and by placing them with respect to the detected anchor pose. For item 5 (1220), the UE records the render-start-time when the updated scene is ready to be rendered. For item 6 (1222), the UE renders the scene with the predicted pose related to the predicted presentation time provided by the XR Runtime. For item 7 (1224), the UE records the render-end-time when the rendering task is done. The rendered frame is provided to the XR Runtime. For item 8 (1226), the XR Runtime via the composition and warping block 1204 performs further post-processing such as late pose correction and final composition. For item 9 (1228), the rendered frame is presented to the display 1202. The UE records the presentation-time.

[0246] Based on this ADRP measurement call flow, the UE may measure the ADRP for a particular anchor as shown below in Eq. 7:ADRP = presentation time — anchor pose request time (7)

[0247] Depending on the granularity of the ADRP delay for that anchor defined during the configuration step, the UE, for some embodiments, may measure the following processing delays shown in Eqs. 8-11 : anchor detection processing delay = anchor detection time — anchor pose request time (8)render processing delay = render end time — render start time (10) render to photon metric = presentation time — render end time (11)ADRP Measurement for STAR UE with Remote Spatial Computing

[0248] FIG. 13A is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for STAR UE with remote spatial computing according to some embodiments. FIG. 13B is a continuation of the message sequencing diagram of FIG. 13A illustrating an example process for anchor detection to render to photon measurement for STAR UE with remote spatial computing according to some embodiments. For some embodiments, the process 1300 shown in FIGs. 13A- 13B illustrates the case of a UE which performs locally the rendering but cannot detect and track a type of trackable leading to the delegation of the spatial computing function to an edge server.

[0249] For item 1 (1318), the XR Source Management 1306 retrieves (from the XR runtime 1304 for some embodiments) the camera feed or a still picture of the UE surroundings to be used by the Spatial Computing function to detect the trackable. The term camera information will be used in the following steps to represent the camera feed or a still picture. For item 2 (1320), the camera information is provided to the Media Access Function (MAF) 1312 for encoding. The UE appends a unique anchor identifier (anchor-id) and the encode- start-time to the ADRP QoE metadata structure. For item 3 (1322), the camera information is encoded and ready to be sent to the XR Server. The UE appends the encode-end-time to the ADRP QoE metadata structure. For item 4 (1324), the XR Server receives via the media delivery function 1314 the encoded camera information and starts the decoding process. The XR Server appends the decode-start-time to the ADRP QoE metadata structure.

[0250] For item 5 (1326), the XR Server decodes the encoded camera information. For item 6 (1328), the decoded camera information is provided to the Spatial Computing function 1316 for requesting the pose of the anchor. The XR Server appends the anchor-pose-request-time to the ADRP QoE metadata structure. For item 7 (1330), the XR Spatial Computing function 1316 detects the anchor. The XR Server sends the predicted pose of the anchor to the UE. The XR Server appends a TRUE anchor-detection Boolean and the anchor-detection-time to the ADRP QoE metadata structure. For item 8 (1332), the UE (via the scene graph handler 1310) receives the predicted pose of the anchor and starts the update of the scene. The UE appends the update-start-time to the ADRP QoE metadata structure.

[0251] For item 9 (1334), the UE updates the scene by enabling the virtual assets related to that anchor and by placing them with respect to the detected anchor pose. For item 10 (1336), the UE appends the render-start-time to the ADRP QoE metadata structure when the updated scene is ready to be rendered bythe visual Tenderer 1308. For item 11 (1338), the UE renders the scene with the predicted pose related to the predicted presentation time provided by the XR Runtime. For item 12 (1340), the UE appends the render- end-time to the ADRP QoE metadata structure when the rendering task is done. The rendered frame is provided to the XR Runtime. For item 13 (1342), the XR Runtime performs further post-processing such as late pose correction and final composition. For item 14 (1344), the rendered frame is presented to the display 1302. The UE appends the presentation-time to the ADRP QoE metadata structure.

[0252] Based on this ADRP measurement call flow, the UE may measure the ADRP 1346 for a particular anchor as shown in Eq. 12:ADRP = presentation time — anchor pose request time (12)

[0253] Depending on the granularity of the ADRP delay for that anchor defined during the configuration step, the UE, for some embodiments, may measure the following processing delays in Eqs. 13-20: camera information encoding delay = encode end time — encode start time (13) network oplink delay = decode start time — encode end time (14) camera information decoding delay = anchor pose request time — decode start time (15) anchor detection processing delay = anchor detection time — anchor pose request time (16) network downlink delay = update start time — anchor detection time (17) update processing delay = render start time — update start time (18) render processing delay = render end time — render start time (19) render to photon metric = presentation time — render end time (20)

[0254] The RTP header extension or the RTCP may be used to carry the ADRP QoE metadata structure when the structure is associated with the camera information in the uplink. The WebRTC data channel may be used to send the ADRP QoE metadata structure with the anchor pose response in the downlink. Each of these pieces of delay information may be used to adjust the AR anchoring process before further spatial computing processing for that anchor on that server.ADRP Measurement for EDGAR UE with Remote Spatial Computing and Rendering

[0255] FIG. 14A is a message sequencing diagram illustrating an example process for anchor detection to render to photon measurement for EDGAR UE with remote spatial computing and rendering according to some embodiments. FIG. 14B is a continuation of the message sequencing diagram of FIG. 14A illustratingan example process for anchor detection to render to photon measurement for EDGAR UE with remote spatial computing and rendering according to some embodiments.

[0256] For item 1 (1420) of the process 1400, the XR Source Management 1406 retrieves (from the XR runtime 1404 for some embodiments) the camera feed or a still picture of the UE surroundings to be used by the Spatial Computing function to detect the trackable. The term camera information will be used in the following steps to represent the camera feed or a still picture. For item 2 (1422), the camera information is provided to the Media Access Function (MAF) 1410 for encoding. The UE appends a unique anchor identifier (anchor-id) and the encode-start-time to the ADRP QoE metadata structure. For item 3 (1424), the camera information is encoded and ready to be sent to the XR Server. The UE appends the encode-end-time to the ADRP QoE metadata structure. For item 4 (1426), the media delivery function 1412 of the XR Server receives from the media access function 1410 the encoded camera information and starts the decoding process. The XR Server appends the decode-start-time to the ADRP QoE metadata structure. For item 5 (1428), the XR Server decodes the encoded camera information.

[0257] For item 6 (1430), the decoded camera information is provided to the Spatial Computing function for requesting the pose of the anchor. The XR Server appends the anchor-pose-request-time to the ADRP QoE metadata structure. For item 7 (1432), the XR Spatial Computing function detects the anchor. The XR Server appends a TRUE anchor-detection Boolean and the anchor-detection-time to the ADRP QoE metadata structure. For item 8 (1434), the Scene Graph Handler 1416 receives (from the XR spatial computing block 1418) the predicted pose of the anchor and starts the update of the scene. The XR Server appends the update-start-time to the ADRP QoE metadata structure. For item 9 (1436), the Scene Graph Handler 1416 updates the scene by enabling the virtual assets related to that anchor and by placing them with respect to the detected anchor pose. For item 10 (1438), the XR Server appends the render-start-time to the ADRP QoE metadata structure when the updated scene is ready to be rendered.

[0258] For item 11 (1440), the XR Server (via the visual Tenderer 1414) renders the scene with the predicted pose related to the predicted presentation time provided by the XR Runtime. For item 12 (1442), the XR Server appends the render-end-time to the ADRP QoE metadata structure when the rendering task is done. The rendered frame is provided to the Media Delivery Function. For item 13 (1444), the rendered frame is encoded and ready to be sent to the UE. The XR Server appends the rendered-frame-encode-end- time to the ADRP QoE metadata structure. For item 14 (1446), the UE receives the encoded rendered frame and starts the decoding process. The UE appends the rendered-frame-decode-start-time to the ADRP QoE metadata structure. For item 15 (1448), the UE decodes the encoded rendered frame. The UE appends the rendered-frame-decode-end-time to the ADRP QoE metadata structure.

[0259] For item 16 (1450), the rendered frame is provided to the lightweight Scene Graph Handler 1408 for the view configuration. For item 17 (1452), the rendered frame is provided to the XR Runtime 1404. For item 18 (1454), the XR Runtime 1404 performs further post-processing such as late pose correction and final composition. For item 19 (1456), the rendered frame is presented to the display 1402. The UE appends the presentation-time to the ADRP QoE metadata structure.

[0260] Based on this ADRP measurement call flow, the UE may measure the ADRP 1458 for a particular anchor as shown in Eq. 21 :ADRP = presentation time - anchor pose request time (21)

[0261] Depending on the granularity of the ADRP delay for that anchor defined during the configuration step, the UE, for some embodiments, may measure the following processing delays in Eqs. 22-30: camera information encoding delay = encode end time — encode start time (22) network uplink delay = decode start time — encode end time (23) camera information decoding delay = anchor pose request time — decode start time (24) anchor detection processing delay = anchor detection time — anchor pose request time (25) update processing delay = render start time — update start time (26) render processing delay = render end time — render start time (27) rendered frame encoding delay = rendered frame encode end time — render end time (28) network DL delay = rendered frame decode start time — rendered frame encode end time (29) rendered frame dec. delay = rendered fr. dec. end time — rendered frame dec. start time (30)

[0262] The RTP header extension or the RTCP may be used to carry the ADRP QoE metadata structure when the structure is associated with the camera information in the uplink and with the rendered frame in the downlink. Each of these pieces of delay information may be used to adjust the AR anchoring process before further spatial computing processing for that anchor on that server.Measurement of Tracking-Based QoE Metrics

[0263] Measure of tracking-based QOE metrics occurs after the anchor detection and after a potential AR anchoring process adjustment following the ADRP measurement. The measurement parameters (e.g., periodicity, observation period) of the tracking based QoE metrics have been defined during the configuration step. These metrics are measured either in the UE or in the XR server depending on the location of the spatial computing function for that anchor. The measurement results collected either by the UE or the XRserver may be used to adjust the AR anchoring process for that particular anchor. The AUR and PPE metrics are detailed below.AUR Metric Measurement

[0264] The Anchor Untracked Ratio (AUR) metric corresponds to the ratio between the number of frames where the trackable associated with the anchor is not tracked and the total number of frames during the observation period in which no detection process is launched. For some embodiments, the complementary Anchor Tracked Ratio (ATR) may be used. The relationship between the AUR and ATR metrics is shown in Eq. 31 :ATR = 1.0 - AUR (31)

[0265] As an example, the Khronos OpenXR API may be used to determine the tracked / untracked status of a trackable with the xrSpaceLocationFlagBits of the xrLocateSpace() function. The anchor is reported as untracked if the bitwise comparison of (XR_SPACE_LOCATION_POSITION_TRACKED_BIT & XrSpaceLocationFlags) or (XR_SPACE_LOCATION_ORIENTATION_TRACKED_BIT & XrSpaceLocationFlags) is unset.TPPE Metric Measurement

[0266] The Trackable Pose Prediction Error (TPPE) spatial metric corresponds to the difference between the predicted pose (position and orientation) of the trackable calculated in the spatial computing function and the pose of that trackable in the user's real environment. As an example, the Khronos OpenXR API (described above) may be used to determine the predicted pose of a trackable with the xrLocateSpace() function.Static Trackable

[0267] A static trackable is a trackable that remains at a fixed position in the user environment. To be able to compare this predicted pose with the real pose, some embodiments may use information from other UEs. The trackable is also tracked by other UEs or by other external tracking systems sharing a common parent XR space in which the difference between the different poses may be calculated. The base (origin, 3 axes) of the XR space related to the trackable is displayed on the UE AR device, and, for some embodiments, the user may estimate / measure the difference if the trackable real pose is easily recognizable (e.g., in the case of a 2D marker).Moving Trackable

[0268] A Moving trackable is a trackable that moves in the user environment (e.g., user right hand). For that type of trackable, the TPPE will tend to decrease as the presentation time for which the prediction is done is closer to the current time. The TPPE may be estimated a posteriori, once the anchor with the predicted pose has been displayed to the user after the presentation time, by using the following procedure for some embodiments. The XR Runtime predicts for the next rendered frame a presentation time called presentation-time. The predicted Trackable Pose, called TP.predictedl , is queried to the spatial computing at the presentation-time. This action may be performed by calling the function xrLocateSpace() in OpenXR. The application performs all the process to update and render the scene. And the rendered frame is provided to the XR Runtime. The spatial computing performs further post-processing such as late pose correction and final composition. The rendered frame is presented to the display at the presentation-time. The application queries for an up-to-date Trackable Pose. This can be achieved by calling the xrLocateSpace() function in OpenXR. The spatial computing returns the a posteriori Trackable Pose called TP.predicted2 for the presentation-time.

[0269] The TPPE may be computed as shown in Eq. 32:TPPE = TP.predictedl - TP. predicted2 (32)Adjustment of AR Anchoring Process

[0270] For some embodiments, the framework provides different metrics measurements at several steps of the AR anchoring process, thereby allowing several types of adjustments for each anchor. For delay / latency related QoE metrics (e.g., ACD, ADRP), the configurable granularity of the delay measurement provides the ability to identify where the most time is spent and to be able to make corresponding adjustments.

[0271] The UE may request improved QoS for the uplink and / or downlink flows if the network transmission delay is significant, such as above a threshold. The XR content may be adapted (e.g., with some level-of- detail mechanism(s)) if the update and / or rendering processing delay is significant, such as above a threshold. The level and the quality of media encoding / decoding may be adapted to reduce the related delay. These adjustments may be performed after each consecutive QoE metric measurement (e.g., after ACD and before ADRP and then after ADRP and before AUR), thereby allowing rapid adaptation. Tracking-based QoE metrics may point out problems with detection / tracking of a trackable. As an example, if the AUR is higher than a given threshold, the adjustment may include displaying a warning message to notify the user, such as a message to move to a more appropriate location (closer to the trackable) and / or to charge a battery if battery level has a strong impact on the performance of the spatial computing function. Another potentialadj ustment in the case of a high AUR may be to select a fallback trackable which is easier to detect than the current trackable. A list of fallback trackables may be pre-defined and provided to the UE in the scene description file for some embodiments.

[0272] FIG. 15 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments. For some embodiments, an example process 1500 may include obtaining 1502 augmented reality (AR) anchoring information related to a real-world user environment. For some embodiments, the example process 1500 may further include configuring 1504 at least one quality of experience (QoE) metric based on AR anchoring information. For some embodiments, the example process 1500 may further include measuring 1506 the at least one creation-based QoE metric. For some embodiments, the example process 1500 may further include measuring 1508 at least one detection-based QoE metric. For some embodiments, the example process 1500 may further include measuring 1510 at least one tracking-based QoE metric.

[0273] FIG. 16 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments. For some embodiments, an example process 1600 may include requesting 1602 an anchor pose. For some embodiments, the example process 1600 may further include detecting 1604 an anchor. For some embodiments, the example process 1600 may further include updating 1606 a scene based on the anchor pose. For some embodiments, the example process 1600 may further include rendering 1608 the scene. For some embodiments, the example process 1600 may further include presenting 1610 the scene. For some embodiments, the example process 1600 may further include determining 1612 an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0274] FIG. 17 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments. For some embodiments, an example process 1700 may include obtaining 1702 camera information. For some embodiments, the example process 1700 may further include encoding 1704 the camera information. For some embodiments, the example process 1700 may further include sending 1706 the encoded camera information to a server. For some embodiments, the example process 1700 may further include receiving 1708 an anchor pose. For some embodiments, the example process 1700 may further include updating 1710 a scene based on the received anchor pose. For some embodiments, the example process 1700 may further include rendering 1712 the scene. For some embodiments, the example process 1700 may further include presenting 1714 the scene. For some embodiments, the example process 1700 may further include determining 1716 an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0275] FIG. 18 is a flowchart illustrating an example process for an AR anchoring QoE framework according to some embodiments. For some embodiments, an example process 1800 may include obtaining 1802 camera information. For some embodiments, the example process 1800 may further include encoding 1804 the camera information. For some embodiments, the example process 1800 may further include sending 1806 the encoded camera information to a server. For some embodiments, the example process 1800 may further include receiving 1808 a rendered frame. For some embodiments, the example process 1800 may further include decoding 1810 the rendered frame. For some embodiments, the example process 1800 may further include presenting 1812 the decoded frame. For some embodiments, the example process 1800 may further include determining 1814 an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the decoded frame is presented and when an associated anchor pose is requested.

[0276] A User Equipment (UE) may correspond to any extended Reality (XR) device / node which may come in variety of form factors. Typical UE (e.g., XR UE) may include, but not limited to the following: Head Mounted Displays (HMD), optical see-through glasses and video see-through HMDs for Augmented Reality (AR) and Mixed Reality (MR), mobile devices with positional tracking and camera, wearables etc. In addition to the above, several different types of XR UE may be envisioned based on XR device functions for e.g., as display, camera, sensors, sensor processing, wireless connectivity, XR / Media processing, and power supply, to be provided by one or more devices, wearables, actuators, controllers and / or accessories. One or more device / nodes / UEs may be grouped into a collaborative XR group for supporting any XR application / experience / service.

[0277] While the methods and systems in accordance with some embodiments are generally discussed in context of extended reality (XR), some embodiments may be applied to any XR contexts such as, e.g., virtual reality (VR) / mixed reality (MR) / augmented reality (AR) contexts. Also, although the term "head mounted display (HMD)” is used herein in accordance with some embodiments, some embodiments may be applied to a wearable device (which may or may not be attached to the head) capable of, e.g., XR, VR, AR, and / or MR for some embodiments.

[0278] An example method in accordance with some embodiments may include: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; measuring the at least one creation-based QoE metric; measuring at least one detection-based QoE metric; and measuring at least one tracking-based QoE metric.

[0279] For some embodiments of the example method, the at least one creation-based QoE metric is an anchor creation delay (ACD) measurement, and measuring the at least one creation-based QoE metric mayinclude: measuring a creation-based start time; measuring a creation-based end time; and determining the at least one creation-based QoE metric based on the creation-based start time and the creation-based end time.

[0280] For some embodiments of the example method, the at least one detection-based QoE metric is an anchor detection to render to photon (ADRP) measurement, and measuring the at least one detection-based QoE metric may include: measuring a presentation time; measuring an anchor pose request time; and determining the at least one detection-based QoE metric based on the presentation time and the anchor pose request time.

[0281] For some embodiments of the example method, the at least one tracking-based QoE metric is based on a static trackable in a user environment, and the static trackable remains in a fixed position within the user environment.

[0282] For some embodiments of the example method, the at least one tracking-based QoE metric is based on a moving trackable in a user environment, and the moving trackable changes position within the user environment.

[0283] Some embodiments of the example method may further include: obtaining tracking information from a separate user equipment device, measuring the at least one tracking-based QoE metric is based on the tracking information from the separate user equipment device.

[0284] For some embodiments of the example method, measuring the at least one tracking-based QoE metric comprises using at least one trackable entity, and the at least one trackable object is a model of an element in a real-world user environment.

[0285] For some embodiments of the example method, measuring the at least one tracking-based QoE metric is based on at least one anchor entity, the at least one anchor entity is a real-world pose, and the method further comprises using the at least one trackable entity to identify the real-world pose corresponding to the at least one anchor entity.

[0286] Some embodiments of the example method may further include measuring the at least one tracking-based QoE metric comprises measuring a location of a trackable entity within a real-world user environment.

[0287] For some embodiments of the example method, measuring the location of the trackable entity within the real-world user environment is based on an anchor corresponding to a real-world pose.

[0288] Some embodiments of the example method may further include: measuring a trackable pose prediction error (TPPE), wherein the TPPE corresponds to a difference between a predicted pose of a trackable entity and an actual pose of the trackable entity in a real-world user environment, and measuring the at least one tracking-based QoE metric is based on measuring a pose of the trackable entity.

[0289] Some embodiments of the example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is needed, cycling back to re-measure the at least one detection-based QoE metric.

[0290] Some embodiments of the example method may further include: determining if trackable detection is needed; and responsive to determining that trackable detection is not needed, cycling back to re-measure the at least one tracking-based QoE metric.

[0291] Some embodiments of the example method may further include adjusting an AR anchoring process.

[0292] For some embodiments of the example method, adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

[0293] For some embodiments of the example method, adjusting the AR anchoring process is based on at least one of the at least one creation-based QoE metric, the at least one detection-based QoE metric, and the at least one tracking-based QoE metric.

[0294] For some embodiments of the example method, adjusting the AR anchoring process comprises selecting a new trackable entity related to measuring the at least one tracking-based QoE metric.

[0295] For some embodiments of the example method, selecting the new trackable entity comprises selecting the new trackable entity from a list included in a scene description file.

[0296] Some embodiments of the example method may further include displaying a warning message corresponding to at least one of the at least one creation-based QoE metric, the at least one detection-based QoE metric, and the at least one tracking-based QoE metric.

[0297] An example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0298] A second example method in accordance with some embodiments may include: requesting an anchor pose; detecting an anchor; updating a scene based on the anchor pose; rendering the scene;presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0299] A second example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0300] A third example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving an anchor pose; updating a scene based on the received anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

[0301] A third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0302] A fourth example method in accordance with some embodiments may include: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving a rendered frame; decoding the rendered frame; presenting the decoded frame; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the decoded frame is presented and when an associated anchor pose is requested.

[0303] A fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0304] An example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the methods listed above.

[0305] An example apparatus in accordance with some embodiments may include a computer-readable medium storing instructions for causing one or more processors to perform any one of the methods listed above.

[0306] An example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the methods listed above.

[0307] An example computer-readable medium in accordance with some embodiments may store a scene description file generated according to any one of the methods listed above.

[0308] An example signal in accordance with some embodiments may include a scene description file generated according to any one of the methods listed above.

[0309] This disclosure describes a variety of aspects, including tools, features, embodiments, models, approaches, etc. Many of these aspects are described with specificity and, at least to show the individual characteristics, are often described in a manner that may sound limiting. However, this is for purposes of clarity in description, and does not limit the disclosure or scope of those aspects. Indeed, all of the different aspects can be combined and interchanged to provide further aspects. Moreover, the aspects can be combined and interchanged with aspects described in earlier filings as well.

[0310] The aspects described and contemplated in this disclosure can be implemented in many different forms. While some embodiments are illustrated specifically, other embodiments are contemplated, and the discussion of particular embodiments does not limit the breadth of the implementations. At least one of the aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded. These and other aspects can be implemented as a method, an apparatus, a computer readable storage medium having stored thereon instructions for encoding or decoding video data according to any of the methods described, and / or a computer readable storage medium having stored thereon a bitstream generated according to any of the methods described.

[0311] In the present disclosure, the terms "reconstructed” and "decoded” may be used interchangeably, the terms "pixel” and "sample” may be used interchangeably, the terms "image,” "picture” and "frame” may be used interchangeably. Usually, but not necessarily, the term "reconstructed” is used at the encoder side while "decoded” is used at the decoder side.

[0312] The terms HDR (high dynamic range) and SDR (standard dynamic range) often convey specific values of dynamic range to those of ordinary skill in the art. However, additional embodiments are also intended in which a reference to HDR is understood to mean "higher dynamic range” and a reference to SDR is understood to mean "lower dynamic range.” Such additional embodiments are not constrained by any specific values of dynamic range that might often be associated with the terms "high dynamic range” and "standard dynamic range.”

[0313] Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and / or use of specific steps and / or actions may be modified or combined.Additionally, terms such as "first”, "second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., such as, for example, a "first decoding” and a "second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.

[0314] Various numeric values may be used in the present disclosure, for example. The specific values are for example purposes and the aspects described are not limited to these specific values.

[0315] Embodiments described herein may be carried out by computer software implemented by a processor or other hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The processor can be of any type appropriate to the technical environment and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as nonlimiting examples.

[0316] Various implementations involve decoding. "Decoding”, as used in this disclosure, can encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display. In various embodiments, such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding. In various embodiments, such processes also, or alternatively, include processes performed by a decoder of various implementations described in this disclosure, for example, extracting a picture from a tiled (packed) picture, determining an upsampling filter to use and then upsampling a picture, and flipping a picture back to its intended orientation.

[0317] As further examples, in one embodiment "decoding” refers only to entropy decoding, in another embodiment "decoding” refers only to differential decoding, and in another embodiment "decoding” refers to a combination of entropy decoding and differential decoding. Whether the phrase "decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions.

[0318] Various implementations involve encoding. In an analogous way to the above discussion about "decoding”, "encoding” as used in this disclosure can encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream. In various embodiments, such processes include one or more of the processes typically performed by an encoder, for example, partitioning, differential encoding, transformation, quantization, and entropy encoding. In variousembodiments, such processes also, or alternatively, include processes performed by an encoder of various implementations described in this disclosure.

[0319] As further examples, in one embodiment "encoding” refers only to entropy encoding, in another embodiment "encoding” refers only to differential encoding, and in another embodiment "encoding” refers to a combination of differential encoding and entropy encoding. Whether the phrase "encoding process” is intended to refer specifically to a subset of operations or generally to the broader encoding process will be clear based on the context of the specific descriptions.

[0320] When a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method / process.

[0321] Various embodiments refer to rate distortion optimization. In particular, during the encoding process, the balance or trade-off between the rate and distortion is usually considered, often given the constraints of computational complexity. The rate distortion optimization is usually formulated as minimizing a rate distortion function, which is a weighted sum of the rate and of the distortion. There are different approaches to solve the rate distortion optimization problem. For example, the approaches may be based on an extensive testing of all encoding options, including all considered modes or coding parameters values, with a complete evaluation of their coding cost and related distortion of the reconstructed signal after coding and decoding. Faster approaches may also be used, to save encoding complexity, in particular with computation of an approximated distortion based on the prediction or the prediction residual signal, not the reconstructed one. A mix of these two approaches can also be used, such as by using an approximated distortion for only some of the possible encoding options, and a complete distortion for other encoding options. Other approaches only evaluate a subset of the possible encoding options. More generally, many approaches employ any of a variety of techniques to perform the optimization, but the optimization is not necessarily a complete evaluation of both the coding cost and related distortion.

[0322] The implementations and aspects described herein can be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed can also be implemented in other forms (for example, an apparatus or program). An apparatus can be implemented in, for example, appropriate hardware, software, and firmware. The methods can be implemented in, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processorsalso include communication devices, such as, for example, computers, cell phones, portable / personal digital assistants ("PDAs”), and other devices that facilitate communication of information between end-users.

[0323] Reference to "one embodiment” or "an embodiment” or "one implementation” or "an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase "in one embodiment” or "in an embodiment” or "in one implementation” or "in an implementation”, as well any other variations, appearing in various places throughout this disclosure are not necessarily all referring to the same embodiment.

[0324] Additionally, this disclosure may refer to "determining” various pieces of information. Determining the information can include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

[0325] Further, this disclosure may refer to "accessing” various pieces of information. Accessing the information can include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0326] Additionally, this disclosure may refer to "receiving” various pieces of information. Receiving is, as with "accessing”, intended to be a broad term. Receiving the information can include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, "receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0327] It is to be appreciated that the use of any of the following "and / or”, and "at least one of, for example, in the cases of “A / B”, "A and / or B” and "at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of "A, B, and / or C” and "at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended for as many items as are listed.

[0328] Also, as used herein, the word "signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a particular one of a plurality of parameters for region-based filter parameter selection for de-artifact filtering. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word "signal”, the word "signal” can also be used herein as a noun.

[0329] Implementations can produce a variety of signals formatted to carry information that can be, for example, stored or transmitted. The information can include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal can be formatted to carry the bitstream of a described embodiment. Such a signal can be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries can be, for example, analog or digital information. The signal can be transmitted over a variety of different wired or wireless links, as is known. The signal can be stored on a processor-readable medium.

[0330] Note that various hardware elements of one or more of the described embodiments are referred to as "modules” that carry out (i.e., perform, execute, and the like) various functions that are described herein in connection with the respective modules. As used herein, a module includes hardware (e.g., one or more processors, one or more microprocessors, one or more microcontrollers, one or more microchips, one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more memory devices) deemed suitable by those of skill in the relevant art for a given implementation. Each described module may also include instructions executable for carrying out the one or more functions described as being carried out by the respective module, and it is noted that those instructions could take the form of or include hardware (i.e., hardwired) instructions, firmware instructions, software instructions, and / or the like, and may be stored in any suitable non-transitory computer-readable medium or media, such as commonly referred to as RAM, ROM, etc.

[0331] Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

CLAIMS1. A method comprising: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one tracking-based QoE metric.

2. The method of claim 1 , wherein the at least one tracking-based QoE metric is based on a static trackable in a user environment, and wherein the static trackable remains in a fixed position within the user environment.

3. The method of any one of claims 1-2, wherein the at least one tracking-based QoE metric is based on a moving trackable in a user environment, and wherein the moving trackable changes position within the user environment.

4. The method of any one of claims 2-3, further comprising: obtaining tracking information from a separate user equipment device, wherein measuring the at least one tracking-based QoE metric is based on the tracking information from the separate user equipment device.

5. The method of any one of claims 1-4, wherein measuring the at least one tracking-based QoE metric comprises using at least one trackable entity, and wherein the at least one trackable object is a model of an element in a real-world user environment.

6. The method of claim 5, wherein measuring the at least one tracking-based QoE metric is based on at least one anchor entity, wherein the at least one anchor entity is a real-world pose, and wherein the method further comprises using the at least one trackable entity to identify the real- world pose corresponding to the at least one anchor entity.

7. The method of any one of claims 1-6, further comprising measuring the at least one tracking-based QoE metric comprises measuring a location of a trackable entity within a real-world user environment.

8. The method of claim 7, wherein measuring the location of the trackable entity within the real-world user environment is based on an anchor corresponding to a real-world pose.

9. The method of any one of claims 1-8, further comprising: measuring a trackable pose prediction error (TPPE), wherein the TPPE corresponds to a difference between a predicted pose of a trackable entity and an actual pose of the trackable entity in a real-world user environment, and wherein measuring the at least one tracking-based QoE metric is based on measuring a pose of the trackable entity.

10. The method of any one of claims 1-9, further comprising: determining if trackable detection is needed; and responsive to determining that trackable detection is needed, cycling back to re-measure the at least one detection-based QoE metric.

11. The method of any one of claims 1-10, further comprising: determining if trackable detection is needed; and responsive to determining that trackable detection is not needed, cycling back to re-measure the at least one tracking-based QoE metric.

12. The method of any one of claims 1-11 , further comprising adjusting an AR anchoring process.

13. The method of claim 12, wherein adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

14. The method of any one of claims 12-13, wherein adjusting the AR anchoring process is based on the at least one tracking-based QoE metric.

15. The method of any one of claims 12-14, wherein adjusting the AR anchoring process comprises selecting a new trackable entity related to measuring the at least one tracking-based QoE metric.

16. The method of claim 15, wherein selecting the new trackable entity comprises selecting the new trackable entity from a list included in a scene description file.

17. The method of any one of claims 1-16, further comprising displaying a warning message corresponding to the at least one tracking-based QoE metric.

18. The method of any one of claims 1-17, wherein the at least one tracking-based QoE metric comprises an Anchor Untracked Ratio (AUR) metric.

19. The method of any one of claims 1-17, wherein the at least one tracking-based QoE metric comprises aTrackable Pose Prediction Error (TPPE) metric.

20. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 1 through 19.21 . A method comprising: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one creation-based QoE metric.

22. The method of claim 21 , wherein the at least one creation-based QoE metric is an anchor creation delay (ACD) measurement, and wherein measuring the at least one creation-based QoE metric comprises: measuring a creation-based start time; measuring a creation-based end time; and determining the at least one creation-based QoE metric based on the creation-based start time and the creation-based end time.

23. The method of any one of claims 21-22, further comprising adjusting an AR anchoring process.

24. The method of claim 23, wherein adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

25. The method of any one of claims 23-24, wherein adjusting the AR anchoring process is based on the creation-based QoE metric.

26. The method of any one of claims 21-25, further comprising displaying a warning message corresponding to the at least one creation-based QoE metric.

27. The method of any one of claims 21-26, wherein the at least one creation-based QoE metric comprises an Anchor Creation Delay (ACD) metric.

28. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 21 through 27.

29. A method comprising: obtaining augmented reality (AR) anchoring information related to a real-world user environment; configuring at least one quality of experience (QoE) metric based on AR anchoring information; and measuring the at least one QoE metric, wherein the at least one QoE metric comprises at least one detection-based QoE metric.

30. The method of claim 29, wherein the at least one detection-based QoE metric is an anchor detection to render to photon (ADRP) measurement, and wherein measuring the at least one detection-based QoE metric comprises: measuring a presentation time; measuring an anchor pose request time; and determining the at least one detection-based QoE metric based on the presentation time and the anchor pose request time.31 . The method of any one of claims 29-30, further comprising adjusting an AR anchoring process.

32. The method of claim 31 , wherein adjusting the AR anchoring process comprises adjusting a level of detail of content related to rendering of an augmented reality user environment.

33. The method of any one of claims 31-32, wherein adjusting the AR anchoring process is based on the at least one detection-based QoE metric.

34. The method of any one of claims 29-33, further comprising displaying a warning message corresponding to the at least one detection-based QoE metric.

35. The method of any one of claims 29-34, wherein the at least one detection-based QoE metric comprises an Anchor Detection-to-Render-to-Photon (ADRP) metric.

36. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 29 through 35.

37. A method comprising: requesting an anchor pose; detecting an anchor; updating a scene based on the anchor pose; rendering the scene; presenting the scene; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

38. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of claim 37.

39. A method comprising: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving an anchor pose; updating a scene based on the received anchor pose; rendering the scene; presenting the scene; anddetermining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the scene is presented and when the anchor pose is requested.

40. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of claim 39.41 . A method comprising: obtaining camera information; encoding the camera information; sending the encoded camera information to a server; receiving a rendered frame; decoding the rendered frame; presenting the decoded frame; and determining an Anchor Detection-to-Render-to-Photon (ADRP) metric as a difference between when the decoded frame is presented and when an associated anchor pose is requested.

42. An apparatus comprising: a processor; and a memory storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of claim 41 .

43. An apparatus comprising at least one processor configured to perform the method of any one of claims1-19, 21-27, 29-35, 37, 39, and 41.

44. An apparatus comprising a computer-readable medium storing instructions for causing one or more processors to perform the method of any one of claims 1-19, 21-27, 29-35, 37, 39, and 41.

45. An apparatus comprising at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform the method of any one of claims 1-19, 21-27, 29-35, 37, 39, and 41.

46. A computer-readable medium storing a scene description file generated according to any one of claims 1-19, 21-27, 29-35, 37, 39, and 41.

47. A signal including a metric generated according to any one of claims 1-19, 21-27, 29-35, 37, 39, and 41.

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