Quality estimation system, quality estimation method, and program

Abstract

The present invention makes it possible to calculate an appropriate perceived quality value in accordance with various required qualities. This quality estimation system includes: a quality-of-experience (QoE) calculation unit that calculates the QoE on the basis of quality information; a required quality acquisition unit that acquires a required quality related to the quality information; and an adjustment unit that adjusts a parameter to be used for QoE calculation in accordance with the acquired required quality.

Description

Quality estimation system, quality estimation method, and program

[0001] The present disclosure relates to a quality estimation system, a quality estimation method, and a program.

[0002] As a related technique, Patent Document 1 discloses a service quality estimation device that estimates the overall service quality of a two-way multimodal service using multiple media such as audio and video. The service quality estimation device described in Patent Document 1 calculates a video media quality value using a video bit rate, a frame rate, and a video packet invalidity rate. The service quality estimation device also calculates a delay quality value using a delay time in a network. Furthermore, the service quality estimation device calculates an audio media quality value using an audio invalid packet rate. The service quality estimation device calculates a Mean Opinion Score (MOS) value, which is an overall quality evaluation value, based on the video media quality value, the delay quality value, and the audio media quality value.

[0003] Japanese Patent Application Laid-Open No. 2006-128972

[0004] In Patent Document 1, parameters used to calculate the video media quality value, delay quality value, and audio media quality value are given as constants. For example, in Patent Document 1, the delay quality value D(D) is calculated as follows, where D is the delay time: D(D)=a ４ +b ４ ・exp(-D / c ４ ) In the above formula, a ４ , b ４ , and c ４ is a constant.

[0005] When the required quality differs between one application and another, the user's quality of experience is considered to differ depending on the required quality. However, in Patent Document 1, if the delay time is the same, the calculated delay quality value shows the same value for both applications. Therefore, even if the calculated quality value is appropriate for one application, the calculated quality value for another application may deviate from the user's quality of experience.

[0006] An exemplary objective of the present disclosure is to provide a quality estimation system, a quality estimation method, and a program that can calculate an appropriate quality of experience value in accordance with various required qualities.

[0007] A quality estimation system according to a first aspect of the present disclosure includes a QoE calculation unit that calculates a Quality of Experience (QoE) based on quality information, a required quality acquisition unit that acquires a required quality related to the quality information, and an adjustment unit that adjusts parameters used to calculate the QoE according to the acquired required quality.

[0008] A quality estimation method according to a second aspect of the present disclosure includes acquiring a required quality related to quality information used to calculate QoE, adjusting parameters used to calculate the QoE according to the acquired required quality, and calculating the QoE based on the quality information.

[0009] A program according to a third aspect of the present disclosure causes a processor to execute a process including acquiring a required quality related to quality information used to calculate QoE, adjusting parameters used to calculate the QoE according to the acquired required quality, and calculating the QoE based on the quality information.

[0010] The quality estimation system, quality estimation method, and program according to the present disclosure can appropriately calculate a quality of experience value in accordance with various required qualities.

[0011] FIG. 1 is a block diagram showing a configuration example of a first quality estimation system according to the present disclosure. FIG. 2 is a flowchart showing an operation procedure in the quality estimation system. FIG. 3 is a block diagram showing a configuration example of a second quality estimation system according to the present disclosure. FIG. 4 is a schematic diagram showing an example of video at a certain time. FIG. 5 is a schematic diagram showing an example of video at another time. FIG. 6 is a graph showing changes over time in network throughput and video QoE. FIG. 7 is a graph showing changes over time in network throughput and video QoE. FIG. 8 is a graph showing changes over time in RTT and communication delay QoE. FIG. 9 is a graph showing changes over time in RTT and communication delay QoE. FIG. 10 is a block diagram showing a configuration example of a third quality estimation system according to the present disclosure. FIG. 11 is a graph showing changes over time in integrated QoE and output QoE. FIG. 12 is a block diagram showing a configuration example of a computer device.

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Note that the following description and drawings have been omitted and simplified as appropriate for clarity of explanation. In addition, in the following drawings, the same or similar elements are designated by the same reference numerals, and duplicate explanations are omitted as necessary.

[0013] FIG. 1 is a block diagram showing an example configuration of a first quality estimation system according to the present disclosure. The first embodiment will be described using FIG. 1. The quality estimation system 100 shown in FIG. 1 includes a QoE calculation unit 101, a required quality acquisition unit 102, and an adjustment unit 103. The quality estimation system 100 may be configured using, for example, one or more devices, each having one or more processors and one or more memories. In the quality estimation system 100, the one or more processors may execute processing in accordance with instructions read from the one or more memories, thereby realizing at least a portion of the functions of each unit in the quality estimation system 100.

[0014] The QoE calculation unit 101 calculates the QoE based on quality information. The quality information includes, for example, information acquired from an application program executed in the system to be evaluated and information acquired from the network. The QoE calculation unit 101 is not limited to calculating one QoE. The QoE calculation unit 101 may calculate multiple QoEs. For example, the QoE calculation unit 101 may calculate a first QoE based on first quality information and a second QoE based on second quality information. In this case, the first quality information and the second quality information may be the same quality information, may be quality information that partially overlaps, or may be mutually different quality information.

[0015] For example, the quality information includes video quality information and communication quality information. The video quality information includes, for example, a video bit rate. The delay quality information includes, for example, a communication delay time such as a round trip time (RTT). The QoE calculation unit 101 may calculate the video QoE based on the video quality information. The video QoE indicates, for example, a value of the user's quality of experience caused by the video quality. Furthermore, the QoE calculation unit 101 may calculate the communication delay QoE based on the communication quality information. The communication delay QoE indicates a value of the user's quality of experience caused by the communication delay.

[0016] The required quality acquisition unit 102 acquires a required quality related to quality information. For example, in video distribution, the video is controlled so that the image quality of each of one or more regions of interest set within the video is higher than the image quality of regions other than the regions of interest. In this case, the required quality acquisition unit 102 may acquire a target bit rate of the video as the required quality. The required quality acquisition unit 102 may also acquire a required delay time as the required quality. The required quality may be a fixed value, or may change depending on time and circumstances during system operation.

[0017] The adjustment unit 103 adjusts parameters used in calculating the QoE according to the acquired required quality. The parameters may include, for example, a coefficient by which the video bit rate is multiplied in calculating the QoE. In this case, the adjustment unit 103 may adjust the value of the coefficient based on the acquired required quality. More specifically, the adjustment unit 103 may adjust the coefficient by which the video bit rate is multiplied, for example, using the target bit rate acquired as the required quality. Furthermore, the adjustment unit 103 may adjust the parameter of the communication delay QoE based on the ratio between the communication delay time and the required delay time.

[0018] The QoE calculation unit 101 calculates the QoE based on the quality information using the adjusted parameters. When the QoE calculation unit 101 calculates the first QoE and the second QoE, the quality estimation system may include an integration unit that integrates the first QoE and the second QoE. The integration unit may integrate the two QoEs into one QoE, for example, by multiplying the first QoE and the second QoE. The integration unit may integrate the first QoE and the second QoE using addition, subtraction, multiplication, division, or a combination of two or more of these. The quality estimation system 100 may also include a storage unit that stores the QoE as time-series data. In this case, the quality estimation system 100 may also include a QoE output unit that acquires the QoE for a predetermined time range from the storage unit and generates a QoE estimation result based on the acquired QoE.

[0019] Next, the operation procedure will be described. Fig. 2 is a flowchart showing the operation procedure in the quality estimation system 100. The operation procedure in the quality estimation system 100 corresponds to a quality estimation method. The required quality acquisition unit 102 acquires a required quality related to quality information used for calculating QoE (step A1). The adjustment unit 103 adjusts parameters used for calculating QoE according to the required quality acquired in step A1 (step A2). The QoE calculation unit 101 acquires the quality information and calculates QoE based on the acquired quality information (step A3).

[0020] In the first embodiment, the adjustment unit 103 adjusts the parameters used to calculate the QoE based on the acquired required quality. In this way, when the required qualities of one application and another application are different from each other, the QoE calculation unit 101 can calculate an appropriate QoE for each application according to the required quality.

[0021] Next, a second embodiment will be described. Fig. 3 is a block diagram showing an example configuration of a second quality estimation system according to the present disclosure. The quality estimation system 200 shown in Fig. 3 includes a video quality information acquisition unit 210, a communication quality information acquisition unit 211, a video QoE calculation unit 220, a communication delay QoE calculation unit 221, a communication loss QoE calculation unit 222, a video required quality acquisition unit 225, a communication delay required quality acquisition unit 226, a video QoE parameter adjustment unit 230, a communication delay QoE parameter adjustment unit 231, and a QoE integration unit 240. The quality estimation system 200 corresponds to the quality estimation system 100 shown in Fig. 1.

[0022] The second embodiment is an example in which the quality estimation system 200 is applied to a remote monitoring and control system that remotely monitors or remotely controls a mobile object. The remote monitoring and control system includes a mobile object to be monitored and a remote monitoring and control device used to monitor and control the mobile object. The remote monitoring and control device and the mobile object are connected to each other via a network. The mobile object includes, for example, a vehicle such as an automobile, a bus, or a train. The mobile object may be an autonomous vehicle configured to be capable of autonomous driving, a remotely driven vehicle whose driving can be remotely controlled, or a regular vehicle driven by a driver. The mobile object transmits images captured using an imaging device to the remote monitoring and control device via the network. The remote monitoring and control device displays the images received from the mobile object on a display device. The remote monitoring and control device transmits control information to the mobile object as needed.

[0023] In the second embodiment, the QoE is the "Quality of experience in communication between a vehicle and a remote driver (Tele-operated Driving) (ToD QoE)" described in "Quality of experience (QoE) requirements for real-time multimedia services over 5G networks" of the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) GSTR-5GQoE. However, in the second embodiment, parameters for calculating the QoE are adjusted according to the required quality for video and communication delay.

[0024] The video quality information acquisition unit 210 acquires video quality information. The video quality information includes, for example, the vertical and horizontal sizes of the video, the frame rate, the video bit rate, and the refresh interval. The communication quality information acquisition unit 211 acquires communication quality information in the network between the mobile object and the remote monitoring and control device. The communication quality information includes, for example, the number of packet burst losses in the network, the network transmission throughput, and the RTT.

[0025] The video QoE calculation unit 220 calculates the video QoE based on the video quality information. The communication delay QoE calculation unit 221 calculates the communication delay QoE. The communication loss QoE calculation unit 222 calculates the communication loss QoE based on the video quality information and the communication quality information. The communication loss QoE indicates the value of the user's quality of experience caused by video disturbances resulting from communication loss such as packet loss, for example. The video QoE calculation unit 220 and the communication delay QoE calculation unit 221 correspond to the QoE calculation unit 101 shown in FIG. 1 .

[0026] The video required quality acquisition unit 225 acquires the video required quality. The video QoE parameter adjustment unit 230 adjusts the parameters used to calculate the video QoE based on the video required quality. The communication delay required quality acquisition unit 226 acquires the communication delay required quality. The communication delay QoE parameter adjustment unit 231 adjusts the parameters used to calculate the communication delay QoE based on the communication delay required quality. The video required quality acquisition unit 225 and the communication delay required quality acquisition unit 226 correspond to the required quality acquisition unit 102 shown in FIG. 1. The video QoE parameter adjustment unit 230 and the communication delay QoE parameter adjustment unit 231 correspond to the adjustment unit 103 shown in FIG. 1.

[0027] The QoE integrator 240 integrates the video QoE calculated by the video QoE calculator 220, the communication delay QoE calculated by the communication delay QoE calculator 221, and the communication loss QoE calculated by the communication loss QoE calculator 222, and outputs the integrated QoE. For example, the video QoE, communication delay QoE, and communication loss QoE are calculated for each time. The QoE integrator 240 outputs the integrated QoE for each time as time-series data, for example. The integrated QoE can be used to understand or predict network response performance, for example, in quality analysis consulting work. Alternatively, the integrated QoE can be used to control network switching and change network resource allocation depending on the movement route, environmental conditions, and service used by a mobile object.

[0028] The calculation of video QoE will now be described. The video QoE calculation unit 220 calculates I, which indicates video QoE, using, for example, the following formula: Ｖ Calculate. Here, v ０ is a constant, and B ｖ is the video bit rate, H is the vertical size of the video, W is the horizontal size of the video, and F is the frame rate. ｃ is a parameter that is adjusted according to the required quality. ｃ The value of is adjusted according to the required quality.

[0029] Here, the video transmitted from the mobile object to the remote monitoring control device is adaptively controlled. Specifically, the mobile object transmits high-quality video of important areas to the remote monitoring control device, and transmits low-quality video of areas other than the important areas. In this case, the video bit rate can change depending on the video within the shooting area.

[0030] FIG. 4 is a schematic diagram showing an example of video at a certain time. A moving-body video distribution device performs processing such as object detection in the video and sets the area where the object is detected as a region of interest (ROI). In the example of FIG. 4, three regions of interest 300-320 are detected in the video. The video distribution device changes the image quality between the regions of interest 300-320 and other areas. For example, the video distribution device sets the image quality of the areas inside the regions of interest 300-320 to high image quality and sharpens the video within each region of interest. Alternatively, the video distribution device sets the image quality of areas outside the regions of interest 300-320 to low image quality. Image quality adjustment is performed, for example, by adjusting the video bit rate.

[0031] Fig. 5 is a schematic diagram showing an example of video at a different time. In this example, one region of interest 330 is detected in the video. Compared to the example of Fig. 4, the video in Fig. 5 contains fewer regions of interest and each region of interest has a smaller area. Therefore, the video in Fig. 5 can be transmitted from the mobile object to the remote monitoring and control device at a lower video bit rate than the video in Fig. 4.

[0032] When the transmitted video is adaptively controlled, the video bit rate of the entire video changes depending on the occupancy rate of the region of interest. However, when image analysis is performed on the received video in the remote monitoring and control device, since the video within the region of interest has high image quality, the accuracy of the image analysis for the video in Figure 5 is considered to be equivalent to the accuracy of the image analysis for the video in Figure 4. If a low video QoE is calculated for a low video bit rate, the video QoE is considered to deviate from the original quality of experience.

[0033] Therefore, in the second embodiment, the video QoE parameter adjustment unit 230 adjusts the above parameter C ｃ For example, the high quality bit rate within the ROI of the video is adjusted to the maximum bit rate b ｍａｘ The low-quality bit rate outside the ROI is set to the minimum bit rate b ｍｉｎ The video QoE parameter adjustment unit 230 receives the target bit rate b ｔ The video QoE parameter adjustment unit 230 acquires the parameter C ｃ is calculated using the following formula: The above parameter C ｃ indicates the compression efficiency, and when uncompressed, i.e., when the entire image is the ROI region, C ｃ On the other hand, the parameter C ｃ When the image is compressed, that is, when a low-quality area other than the ROI is included in the image, C ｃ >1.

[0034] 6 is a graph showing the time variation of network throughput and video QoE. In the graph shown in FIG. 6, the horizontal axis represents time, and the vertical axis represents throughput and the above-mentioned I Ｖ In FIG. 6, the parameter C ｃ The value of C ｃ = 1.

[0035] 7 is a graph showing the time variation of network throughput and video QoE. In the graph shown in FIG. 7, the horizontal axis represents time, and the vertical axis represents throughput and the above-mentioned I Ｖ In FIG. 7, the parameter C ｃ The value of the maximum bit rate b ｍａｘ , minimum bit rate b ｍｉｎ , and the target bit rate b ｔ is adjusted using

[0036] In Figure 6, when the network throughput decreases as a result of a decrease in the video bit rate at approximately t = 110 ms, the MOS value also decreases. In contrast, in Figure 7, even when the network throughput decreases as a result of a decrease in the video bit rate at approximately t = 110 ms, the decrease in the MOS value is suppressed. In this way, by adjusting parameters according to the required quality, it is possible to suppress a decrease in the MOS value when efficient compression is achieved.

[0037] Next, the calculation of the communication delay QoE will be described. The communication delay QoE calculation unit 221 calculates the value of x using the following formula based on the communication quality information. Here, T is, for example, T = RTT. ｍ is a parameter that is adjusted according to the communication delay required quality. ｍ To, T ｍ = communication delay required quality / 2. In this case, T>2×T ｍ In this case, x>1. The communication delay QoE calculation unit 221 calculates I, which indicates the communication delay QoE, using the following formula, for example: Ｄ Calculate.

[0038] 8 is a graph showing the change over time in RTT and communication delay QoE. In the graph shown in FIG. 8, the horizontal axis represents time, and the vertical axis represents RTT and the above-mentioned I Ｄ , i.e., the MOS value. In Fig. 8, the required communication delay quality, i.e., the required delay time, is set to 20 ms. Fig. 9 is a graph showing the time variation of RTT and communication delay QoE. In Fig. 9, the required delay time is set to 50 ms. Fig. 10 is a graph showing the time variation of RTT and communication delay QoE. In Fig. 10, the required delay time is set to 100 ms.

[0039] In the example of Fig. 8, the RTT included in the communication quality information is large compared to the required delay time of 20 ms. Therefore, the MOS value is generally low. In contrast, in the example of Fig. 9, the required delay time is 50 ms, and the MOS value is generally high. Furthermore, when the RTT increases for only a short time, the MOS value temporarily decreases. In the example of Fig. 10, the required delay time is 100 ms, so the MOS value is generally high. Furthermore, even when the RTT increases for only a short time, the decrease in the MOS value is suppressed compared to Fig. 9.

[0040] In the second embodiment, the video QoE parameter adjustment unit 230 adjusts parameters used to calculate the video QoE in accordance with the required video quality. Furthermore, the communication delay QoE parameter adjustment unit 231 adjusts parameters used to calculate the communication delay QoE in accordance with the required communication delay. The video QoE parameter adjustment unit 230 adjusts the calculated video QoE value, for example, using a target bit rate in adaptive video control. Furthermore, the communication delay QoE parameter adjustment unit 231 adjusts the calculated communication delay QoE value in accordance with the ratio of the observed delay to the required delay. In the second embodiment, the video QoE parameter adjustment unit 230 and the communication delay QoE parameter adjustment unit 231 can adjust the video QoE and communication delay QoE in accordance with various video requirements and various communication delay requirements, respectively.

[0041] A third embodiment of the present disclosure will be described. Fig. 11 is a block diagram showing a configuration example of a third quality estimation system according to the present disclosure. The quality estimation system 200a shown in Fig. 11 includes a storage unit 241 and a QoE output unit 242 in addition to the configuration of the quality estimation system 200 shown in Fig. 3. The storage unit 241 stores the integrated QoE output as time-series data in a storage device. The QoE output unit 242 acquires QoE for a predetermined time range from the storage unit 241. The QoE output unit 242 generates a QoE estimation result based on the acquired QoE and outputs the QoE estimation result as an output QoE.

[0042] The storage unit 241 stores the integrated QoE for the period from time t to time t-Δ, where t is the current time and Δ is a predetermined time. The QoE output unit 242 calculates, for example, the average value of the integrated QoE for the period from time t to time t-Δ. The QoE output unit 242 also examines the magnitude of fluctuations in the integrated QoE for the period from time t to time t-Δ. The QoE output unit 242 counts, for example, the number of times that the fluctuations in the integrated QoE for each time point relative to the average value, or the fluctuations in the integrated QoE between adjacent times, exceed a predetermined threshold. If the counted number exceeds a predetermined value, the QoE output unit 242 reduces the output QoE relative to the average value of the integrated QoE for the predetermined period. The QoE output unit 242 can use a module equivalent to the Temporal module in ITU-T P.1203.

[0043] 12 is a graph showing time-series changes in integrated QoE and output QoE. The inventors evaluated the calculation results of output QoE according to time-series fluctuations in integrated QoE. Time-series data of the MOS value of the integrated QoE was generated using random numbers with a uniform distribution. The average value of the MOS value of the integrated QoE was set to 3. In the 0 ms to 100 ms section and the 200 ms to 300 ms section, the fluctuation range of the MOS value was set to 0.5. On the other hand, in the 100 ms to 200 ms section and the 300 ms to 400 ms section, the fluctuation range of the MOS value was set to 2.

[0044] 12, in the section where the fluctuation of the MOS value is small, the MOS value of the output QoE is approximately the average value of the integrated QoE. In contrast, in the section where the fluctuation of the MOS value is large, the MOS value of the output QoE is approximately the average value of the integrated QoE.

[0045] The quality estimation system 200a uses a moving window to store the most recent integrated QoE in the storage unit 241. The QoE output unit 242 generates an output QoE based on the stored integrated QoE for a predetermined time range. In the third embodiment, an output QoE that takes time-series fluctuations into consideration can be generated from the time-series integrated QoE. Therefore, the third embodiment can calculate the ToD QoE online while taking time-series fluctuations into consideration.

[0046] In the above description, an example has been described in which the QoE output unit 242 generates the output QoE in accordance with time-series fluctuations in the integrated QoE. However, the third embodiment is not limited to this. The QoE output unit 242 may set the average value of the integrated QoE over a predetermined time range as the output QoE. In this case, the QoE output unit 242 functions as a low-pass filter, and can suppress abrupt fluctuations in the output QoE over a short period of time.

[0047] Next, a description will be given of the physical configuration of the quality estimation systems 100 and 200. Fig. 13 is a block diagram showing an example configuration of a computer device that can be used as the quality estimation system 100 or 200. The computer device 500 has a processor 510 such as a CPU (Central Processing Unit), a storage unit 520, a ROM (Read Only Memory) 530, a RAM (Random Access Memory) 540, a communication interface (IF) 550, and a user interface 560.

[0048] The communication interface 550 is an interface for connecting the computer device 500 to a communication network via wired communication means, wireless communication means, etc. The user interface 560 includes a display unit such as a display, and an input unit such as a keyboard, a mouse, and a touch panel.

[0049] The storage unit 520 is an auxiliary storage device that can store various types of data. The storage unit 520 does not necessarily have to be a part of the computer device 500, but may be an external storage device or cloud storage connected to the computer device 500 via a network.

[0050] The ROM 530 is a non-volatile storage device. For example, a semiconductor storage device with a relatively small capacity, such as a flash memory, is used for the ROM 530. The programs executed by the CPU 510 can be stored in the storage unit 520 or the ROM 530. The storage unit 520 or the ROM 530 stores various programs that realize the functions of each unit of the quality estimation system 100 or 200.

[0051] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include RAM, ROM, flash memory, solid-state drive (SSD) or other memory technologies, compact discs (CDs), digital versatile discs (DVDs), Blu-ray discs or other optical disc storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices. The program may also be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0052] The RAM 540 is a volatile storage device. Various semiconductor memory devices such as DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory) are used for the RAM 540. The RAM 540 can be used as an internal buffer for temporarily storing data, etc. The CPU 510 loads a program stored in the storage unit 520 or the ROM 530 into the RAM 540 and executes the loaded program. The CPU 510 executes the program, thereby realizing the functions of each unit in the quality estimation system 100 or 200. The CPU 510 may have an internal buffer for temporarily storing data, etc.

[0053] In the present disclosure, the quality estimation systems 100 and 200 do not necessarily have to be configured as a single computer device. The quality estimation systems 100 and 200 may be configured using multiple physically separated devices. The quality estimation systems 100 and 200 may be configured as an application server, a Mobile / Multi-access Edge Computing (MEC) server, or an in-vehicle device.

[0054] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0055] Each drawing is merely an example for describing one or more embodiments. Each drawing may not relate to only one particular embodiment, but may also relate to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

[0056] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes.

[0057] [Supplementary Note 1] A quality estimation system comprising: a QoE calculation unit that calculates Quality of Experience (QoE) based on quality information; a required quality acquisition unit that acquires a required quality related to the quality information; and an adjustment unit that adjusts parameters used to calculate the QoE according to the acquired required quality.

[0058] [Supplementary Note 2] The quality estimation system according to Supplementary Note 1, wherein the QoE calculation unit calculates a first QoE based on first quality information and calculates a second QoE based on second quality information, and the quality estimation system further comprises an integration unit that integrates the first QoE and the second QoE.

[0059] [Supplementary Note 3] The quality estimation system according to Supplementary Note 1 or 2, wherein the quality information includes video quality information, and the QoE calculated by the QoE calculation unit includes video QoE indicating a user's quality of experience caused by video quality in video distribution.

[0060] [Supplementary Note 4] The quality estimation system according to Supplementary Note 3, wherein the video quality information includes a video bit rate of the video, the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and the adjustment unit adjusts a value of the coefficient based on the required quality.

[0061] [Supplementary Note 5] The quality estimation system according to Supplementary Note 4, wherein the image quality of each of one or more regions of interest set within the image is controlled to be higher than the image quality of regions other than the regions of interest, the required quality acquisition unit acquires a target bit rate of the image as the required quality, and the adjustment unit adjusts the coefficients using the target bit rate.

[0062] [Supplementary Note 6] The quality estimation system according to any one of Supplementary Notes 1 to 5, wherein the quality information includes communication quality information, and the QoE calculated by the QoE calculation unit includes a communication delay QoE indicating a quality experienced by a user due to a communication delay.

[0063] [Supplementary Note 7] The quality estimation system according to Supplementary Note 6, wherein the communication quality information includes a communication delay time, the required quality acquisition unit acquires the required delay time as the required quality, and the adjustment unit adjusts the parameter based on a ratio between the communication delay time and the required delay time.

[0064] [Supplementary Note 8] The quality estimation system according to any one of Supplementary Notes 1 to 7, comprising: a storage unit that stores the QoE as time-series data; and a QoE output unit that acquires the QoE for a predetermined time range from the storage unit and generates an estimation result of the QoE based on the acquired QoE.

[0065] [Supplementary Note 9] A quality estimation method comprising: acquiring a required quality related to quality information used to calculate Quality of Experience (QoE); adjusting parameters used to calculate the QoE according to the acquired required quality; and calculating the QoE based on the quality information.

[0066] [Supplementary Note 10] The quality estimation method according to Supplementary Note 9, comprising: calculating a first QoE based on first quality information; calculating a second QoE based on second quality information; and integrating the first QoE and the second QoE.

[0067] [Supplementary Note 11] The quality estimation method according to Supplementary Note 9 or 10, wherein the quality information includes video quality information, and the QoE includes video QoE indicating a user's quality of experience caused by video quality in video distribution.

[0068] [Supplementary Note 12] The quality estimation method according to Supplementary Note 11, wherein the video quality information includes a video bit rate of the video, and the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and a value of the coefficient is adjusted based on the required quality.

[0069] [Supplementary Note 13] The quality estimation method according to Supplementary Note 12, wherein the image quality of each of one or more regions of interest set in the image is controlled to be higher than the image quality of regions other than the regions of interest, a target bit rate of the image is obtained as the required quality, and the coefficients are adjusted using the target bit rate.

[0070] [Supplementary Note 14] The quality estimation method according to any one of Supplementary Notes 9 to 13, wherein the quality information includes communication quality information, and the QoE includes a communication delay QoE indicating a quality experienced by a user due to a communication delay.

[0071] [Supplementary Note 15] The quality estimation method according to Supplementary Note 14, wherein the communication quality information includes a communication delay time, a required delay time is acquired as the required quality, and the parameter is adjusted based on a ratio between the communication delay time and the required delay time.

[0072] [Supplementary Note 16] The quality estimation method according to any one of Supplementary Notes 9 to 15, further comprising: acquiring the QoE for a predetermined time range from the time-series data of the QoE; and generating an estimation result of the QoE based on the acquired QoE.

[0073] [Supplementary Note 17] A program for causing a processor to execute a process including: acquiring a required quality related to quality information used to calculate Quality of Experience (QoE); adjusting parameters used to calculate the QoE according to the acquired required quality; and calculating the QoE based on the quality information.

[0074] [Supplementary Note 18] The program according to Supplementary Note 17, wherein the processing includes: calculating a first QoE based on first quality information; calculating a second QoE based on second quality information; and integrating the first QoE and the second QoE.

[0075] [Supplementary Note 19] The program according to Supplementary Note 17 or 18, wherein the quality information includes video quality information, and the QoE includes video QoE indicating a user's quality of experience caused by video quality in video distribution.

[0076] [Supplementary Note 20] The program according to Supplementary Note 19, wherein the video quality information includes a video bit rate of the video, the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and a value of the coefficient is adjusted based on the required quality.

[0077] [Supplementary Note 21] The program according to Supplementary Note 20, wherein the image quality of each of one or more regions of interest set in the image is controlled to be higher than the image quality of regions other than the regions of interest, and the processing includes obtaining a target bit rate of the image as the required quality and adjusting the coefficients using the target bit rate.

[0078] [Supplementary Note 22] The program according to any one of Supplementary Notes 17 to 21, wherein the quality information includes communication quality information, and the QoE includes a communication delay QoE indicating a quality experienced by a user due to a communication delay.

[0079] [Supplementary Note 23] The program according to Supplementary Note 22, wherein the communication quality information includes a communication delay time, and the processing includes acquiring a required delay time as the required quality and adjusting the parameter based on a ratio between the communication delay time and the required delay time.

[0080] [Supplementary Note 24] The program according to any one of Supplementary Notes 17 to 23, wherein the processing includes acquiring the QoE for a predetermined time range from time-series data of the QoE, and generating an estimation result of the QoE based on the acquired QoE.

[0081] Some or all of the elements described in any appendix may be applied to a variety of hardware, software, recording means for recording software, systems, and methods.

[0082] This application claims priority based on Japanese Patent Application No. 2024-092753, filed on June 7, 2024, the disclosure of which is incorporated herein in its entirety by reference.

[0083] 100, 200: Quality estimation system 101: QoE calculation unit 102: Required quality acquisition unit 103: Adjustment unit 210: Video quality information acquisition unit 211: Communication quality information acquisition unit 220: Video QoE calculation unit 221: Communication delay QoE calculation unit 222: Communication loss QoE calculation unit 225: Video required quality acquisition unit 226: Communication delay required quality acquisition unit 230: Video QoE parameter adjustment unit 231: Communication delay QoE parameter adjustment unit 240: QoE integration unit 241: Storage unit 242: QoE output unit 500: Computer device 510: Processor 520: Storage unit 530: ROM 540: RAM 550: Communication IF 560: User IF

Claims

1. A quality estimation system comprising: a QoE calculation unit that calculates Quality of Experience (QoE) based on quality information; a required quality acquisition unit that acquires a required quality related to the quality information; and an adjustment unit that adjusts parameters used to calculate the QoE according to the acquired required quality.

2. The quality estimation system according to claim 1, wherein the QoE calculation unit calculates a first QoE based on first quality information and a second QoE based on second quality information, and the quality estimation system further comprises an integration unit that integrates the first QoE and the second QoE.

3. A quality estimation system as described in claim 1 or 2, wherein the quality information includes video quality information, and the QoE calculated by the QoE calculation unit includes video QoE indicating the user's perceived quality due to the video quality in video distribution.

4. The quality estimation system according to claim 3, wherein the video quality information includes a video bit rate of the video, the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and the adjustment unit adjusts the value of the coefficient based on the required quality.

5. The quality estimation system described in claim 4, wherein the video is controlled so that the image quality of each of one or more regions of interest set within the video is higher than the image quality of regions other than the regions of interest, the required quality acquisition unit acquires a target bit rate for the video as the required quality, and the adjustment unit adjusts the coefficient using the target bit rate.

6. A quality estimation system described in any one of claims 1 to 5, wherein the quality information includes communication quality information, and the QoE calculated by the QoE calculation unit includes communication delay QoE indicating the user's quality of experience caused by communication delay.

7. The quality estimation system according to claim 6, wherein the communication quality information includes a communication delay time, the required quality acquisition unit acquires the required delay time as the required quality, and the adjustment unit adjusts the parameter based on the ratio between the communication delay time and the required delay time.

8. A quality estimation system according to any one of claims 1 to 7, comprising: a storage unit that stores the QoE as time-series data; and a QoE output unit that acquires the QoE for a predetermined time range from the storage unit and generates an estimation result of the QoE based on the acquired QoE.

9. A quality estimation method comprising: acquiring a required quality related to quality information used to calculate Quality of Experience (QoE); adjusting parameters used to calculate the QoE according to the acquired required quality; and calculating the QoE based on the quality information.

10. The quality estimation method according to claim 9, comprising: calculating a first QoE based on first quality information; calculating a second QoE based on second quality information; and integrating the first QoE and the second QoE.

11. A quality estimation method according to claim 9 or 10, wherein the quality information includes video quality information, and the QoE includes video QoE indicating the user's quality of experience resulting from the video quality in video distribution.

12. The quality estimation method according to claim 11, wherein the video quality information includes a video bit rate of the video, and the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and the value of the coefficient is adjusted based on the required quality.

13. The quality estimation method according to claim 12, wherein the image quality of each of one or more regions of interest set within the image is controlled to be higher than the image quality of regions other than the regions of interest, a target bit rate of the image is obtained as the required quality, and the coefficients are adjusted using the target bit rate.

14. A quality estimation method according to any one of claims 9 to 13, wherein the quality information includes communication quality information, and the QoE includes communication delay QoE indicating the user's quality of experience caused by communication delay.

15. The quality estimation method according to claim 14, wherein the communication quality information includes a communication delay time, a required delay time is acquired as the required quality, and the parameter is adjusted based on a ratio between the communication delay time and the required delay time.

16. A quality estimation method according to any one of claims 9 to 15, further comprising: acquiring the QoE for a predetermined time range from the time-series data of the QoE; and generating an estimation result of the QoE based on the acquired QoE.

17. A program for causing a processor to execute a process including: acquiring a required quality related to quality information used to calculate Quality of Experience (QoE); adjusting parameters used to calculate the QoE according to the acquired required quality; and calculating the QoE based on the quality information.

18. The program of claim 17, wherein the processing includes: calculating a first QoE based on first quality information; calculating a second QoE based on second quality information; and integrating the first QoE and the second QoE.

19. A program according to claim 17 or 18, wherein the quality information includes video quality information, and the QoE includes video QoE indicating the user's quality of experience resulting from the video quality in video distribution.

20. The program according to claim 19, wherein the video quality information includes a video bit rate of the video, the parameters include a coefficient by which the video bit rate is multiplied in calculating the QoE, and the value of the coefficient is adjusted based on the required quality.

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