Depth adjustment method and electronic equipment

Abstract

The invention provides a depth adjustment method, which comprises the following steps that: in a process that a user watches a target three-dimensional video, a target depth layer is determined, and the target depth layer is determined according to a physiological data sequence generated within a target duration when the user watches the target three-dimensional video and display characteristics of pixels of different depth layers in the target three-dimensional video; determining an attention target of the target three-dimensional video watched by the user at present; and adjusting depth information of pixels in the target three-dimensional video based on the target depth layer and the target of interest.

Description

Technical Field

[0001] This disclosure relates to the field of video processing, and more specifically, to a depth adjustment method and an electronic device. Background Technology

[0002] With the development of 3D display technology and virtual reality technology, 3D video is widely used in film and television entertainment, immersive interaction, and virtual scene presentation. Existing 3D videos typically enhance the stereoscopic effect by providing images with parallax to the left and right eyes to create spatial depth perception. However, in actual viewing, different users have significant differences in their ability to perceive 3D depth and their physiological adaptation. The same 3D video, with fixed depth parameters, may cause visual fatigue, dizziness, or discomfort for some users. Summary of the Invention

[0003] In view of this, the present disclosure provides a depth adjustment method and an electronic device.

[0004] One aspect of this disclosure provides a depth adjustment method, comprising: determining a target depth layer during a user's viewing of a target 3D video, the target depth layer being determined based on a physiological data sequence generated within a target duration of the user's viewing of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video; determining the user's current focus target in the target 3D video; and adjusting the depth information of pixels in the target 3D video based on the target depth layer and the focus target.

[0005] According to embodiments of this disclosure, determining a target depth layer includes: acquiring a physiological data sequence generated within a target duration of a user watching a target 3D video, wherein pixels in the target 3D video have depth information, the pixels are divided into different depth layers according to the depth information, and pixels in different depth layers have different display characteristics; establishing a correspondence between multiple physiological data subsequences in the physiological data sequence and different depth layers based on a first data feature and a display feature of the physiological data sequence; and determining the target depth layer based on a second data feature of the physiological data subsequences corresponding to different depth layers.

[0006] According to embodiments of this disclosure, depth adjustment further includes: acquiring an original video corresponding to the target 3D video, wherein pixels in the original video have depth information, and the pixels are divided into different depth layers according to the depth information; applying different display adjustments to pixels in different depth layers of the original video to obtain the target 3D video, so that when the target 3D video is displayed, pixels corresponding to different depth layers have different display characteristics. According to embodiments of this disclosure...

[0007] According to embodiments of this disclosure, different display adjustments are applied to pixels at different depth layers of the original video to obtain a target 3D video, including: applying dynamic depth perturbations of different ranges to pixels at different depth layers of the original video to obtain a target 3D video; when the target 3D video is displayed, the depth information of pixels at different depth layers changes dynamically within the corresponding range.

[0008] According to embodiments of this disclosure, the target duration is greater than a first duration threshold and less than a second duration threshold. The first duration threshold is greater than the time it takes for the human eye to observe different depth layers in a single observation, and the second duration threshold is less than the time it takes for the human eye to perceive the movement of an object.

[0009] According to embodiments of this disclosure, different display features correspond to different preset physiological data features. Based on the first data feature and the display feature of the physiological data sequence, multiple physiological data subsequences in the physiological data sequence are established to correspond to different depth layers, including: determining the time domain range corresponding to different display features based on the matching result of the first data feature and the preset physiological data feature; and establishing a correspondence between continuous data sequences in different time domain ranges as physiological data subsequences and different depth layers.

[0010] According to embodiments of this disclosure, determining a target depth layer based on second data features of physiological data subsequences corresponding to different depth layers includes: determining physiological comfort evaluation results for users viewing pixels at different depth layers based on second data features of physiological data subsequences corresponding to different depth layers; and determining the target depth layer based on the physiological comfort evaluation results.

[0011] According to embodiments of this disclosure, the second data feature includes at least one of the following: the variance of the physiological data subsequence, the energy intensity of the physiological data subsequence in the first band, the energy intensity of the physiological data subsequence in the second band, the energy intensity of the physiological data subsequence in the third band, and the rate of change of the physiological data subsequence; wherein the first band is positively correlated with the user's level of concentration, the second band is negatively correlated with the user's level of concentration, and the third band is positively correlated with the user's level of fatigue and / or dizziness.

[0012] According to embodiments of this disclosure, adjusting the depth information of pixels in a target 3D video based on a target depth layer and a target of interest includes: determining an adjustment range based on the depth information of the target depth layer and the target of interest, wherein the adjustment range is less than or equal to the depth distance between the pixel corresponding to the target of interest and the target depth layer; adjusting the depth information of each pixel in the target 3D video based on the adjustment range; or, adjusting the depth information of the pixel corresponding to the target of interest in the target 3D video based on the adjustment range.

[0013] Another aspect of this disclosure provides a depth adjustment device, comprising: a first determining module, configured to determine a target depth layer during a user's viewing of a target 3D video, the target depth layer being determined based on a physiological data sequence generated within a target viewing time of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video; a second determining module, configured to determine the user's current focus target in the target 3D video; and a first adjusting module, configured to adjust the depth information of pixels in the target 3D video based on the target depth layer and the focus target.

[0014] Another aspect of this disclosure provides an electronic device including at least one memory for storing a computer program; at least one processor for executing the computer program to perform at least one of the following operations: determining a target depth layer during a user's viewing of a target 3D video, the target depth layer being determined based on a sequence of physiological data generated within a target duration of the user's viewing of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video; determining the user's current focus target in the target 3D video; and adjusting the depth information of pixels in the target 3D video based on the target depth layer and the focus target.

[0015] Another aspect of this disclosure provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform a depth adjustment method according to any of the foregoing embodiments.

[0016] Another aspect of this disclosure provides a computer program product, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the operation of the depth adjustment method of any of the foregoing embodiments. Attached Figure Description

[0017] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1A A flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically;

[0019] Figure 1B This schematically illustrates the depth layer partitioning according to an embodiment of the present disclosure;

[0020] Figure 2 A flowchart illustrating the determination of a target depth layer in a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0021] Figure 3 Another flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically;

[0022] Figure 4A Another flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically;

[0023] Figure 4B This illustration schematically shows the relationship between the target duration and the depth layer according to an embodiment of the present disclosure;

[0024] Figure 5A A flowchart illustrating the correspondence between physiological data subsequences and depth layers in the depth adjustment method according to an embodiment of the present disclosure is shown in the schematic diagram.

[0025] Figure 5B The illustration schematically shows the matching of depth layers with physiological data sequences according to embodiments of the present disclosure;

[0026] Figure 6 This schematically illustrates another flowchart of the depth adjustment method according to an embodiment of the present disclosure for determining a target depth layer;

[0027] Figure 7 A flowchart illustrating depth information of pixels in a whole target 3D video in a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0028] Figure 8 A block diagram schematically illustrates a depth adjustment device according to an embodiment of the present disclosure; and

[0029] Figure 9 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Detailed Implementation

[0030] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0032] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0033] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0034] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0035] Embodiments of this disclosure provide a depth adjustment method, comprising: determining a target depth layer during a user's viewing of a target 3D video, the target depth layer being determined based on a physiological data sequence generated within a target viewing time of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video; determining the user's current focus target in the target 3D video; and adjusting the depth information of pixels in the target 3D video based on the target depth layer and the focus target.

[0036] Figure 1A A flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0037] like Figure 1A As shown, the depth adjustment method may include at least operations S110 to S130.

[0038] During operation S110, while the user is watching the target 3D video, the target depth layer is determined. The target depth layer is determined based on the physiological data sequence generated within the target duration of the user's viewing of the target 3D video, as well as the display characteristics of pixels at different depth layers in the target 3D video.

[0039] Figure 1B The diagram illustrates the depth layer partitioning according to an embodiment of the present disclosure.

[0040] refer to Figure 1BAs shown, the target 3D video exhibits a continuous spatial distribution characteristic in the depth direction. For ease of analysis and processing, this continuous depth space is divided into multiple depth levels, each corresponding to a predetermined depth range, allowing pixels in the video to be categorized into their corresponding depth levels based on their spatial location. The target depth level refers to the depth level deemed more suitable for user viewing or where the user exhibits a better physiological response at the current moment or within a given time period. The physiological data sequence refers to the physiological signals continuously collected within the target duration that reflect the user's response to visual stimuli. Display features are used to characterize the distinguishing attributes of different depth levels in display presentation, thereby ensuring the distinguishability of different depth levels during user perception.

[0041] Specifically, determining the target depth layer is a decision-making process based on correlation. Since pixels at different depth layers possess different display characteristics, the physiological data sequences generated by users when viewing these depth layers contain related response information. By analyzing the correlation between the physiological data sequences and the display characteristics of each depth layer, the overall physiological response state of the user to different depth layers can be assessed, and the target depth layer can be determined among multiple depth layers.

[0042] For example, a user's electroencephalogram (EEG) signals are collected as a physiological data sequence. Different depth layers in a video (e.g., near view, far view) are assigned different visual markers (i.e., display features). By analyzing the responses to these visual markers in the EEG signals, the depth layer corresponding to the visual marker that elicits a specific response from the user (e.g., a specific attentional response or comfort response) is determined as the target depth layer. For example, combined with... Figure 1B The target 3D video is divided into three depth layers: layer 1, layer 2, and layer 3. Physiological data sequences are collected during user viewing, and the correspondence between these physiological data sequences and the display features of layers 1 to 3 is analyzed to determine a target depth layer from layer 1, layer 2, or layer 3.

[0043] In operation S120, the user's current focus in the target 3D video is determined. The focus refers to an object, person, or image area within the current frame of the target 3D video that has a higher degree of visual attraction to the user; it typically constitutes the primary object of focus in the user's current viewing behavior. For example, in a frame of 3D video, a main person or prominent object within a certain depth layer is identified as the focus.

[0044] In operation S130, based on the target depth layer and the target of interest, the depth information of pixels in the target 3D video is adjusted. Adjusting the depth information of pixels in the target 3D video refers to reconfiguring the depth representation relationship of pixels in the video in 3D space, so that the display effect of the target of interest in the depth direction corresponds to the target depth layer. By adjusting the depth based on the target depth layer and the target of interest, the stereoscopic presentation effect of the target of interest is more consistent with the viewing state corresponding to the target depth layer when the user views it.

[0045] For example, when the depth layer corresponding to the target of interest is inconsistent with the target depth layer, the depth representation of the corresponding pixels of the target of interest is adjusted so that the display effect is closer to the depth position corresponding to the target depth layer.

[0046] According to embodiments of this disclosure, by using a depth layer division method, a target depth layer determination mechanism based on physiological data is introduced during the user's viewing process, and the video depth is adjusted in conjunction with the user's focus target. This can overcome the problem of insufficient adaptability caused by the fixed depth setting of traditional 3D videos, and enable the depth presentation of 3D videos to be dynamically optimized according to the viewing status of different users, which helps to reduce discomfort and improve the overall immersive experience.

[0047] Figure 2 A flowchart illustrating the determination of a target depth layer in a depth adjustment method according to an embodiment of the present disclosure is shown.

[0048] like Figure 2 As shown, based on the aforementioned embodiments, operation S110 may include operations S210 to S230.

[0049] In operation S210, physiological data sequences generated during the target viewing time of a target 3D video are acquired. Pixels in the target 3D video possess depth information and are divided into different depth layers based on this information. Pixels in different depth layers exhibit different display characteristics. During this process, pixels in the target 3D video are divided into different depth layers based on their depth information. Each depth layer has distinguishable display characteristics during display, allowing the user to exhibit differentiated response patterns in their physiological data sequences when viewing different depth layers. For example, during a user's continuous viewing of a 3D video, physiological data within the corresponding time period is simultaneously collected as a physiological data sequence. At the same time, pixels in the video frame are divided into multiple depth layers based on depth information, and different depth layers exhibit different display characteristics during display, thus providing a basis for establishing subsequent correlations.

[0050] In operation S220, based on the first data feature and display feature of the physiological data sequence, a correspondence is established between multiple physiological data subsequences in the physiological data sequence and different depth layers. The first data feature is used to describe the feature information in the physiological data sequence that reflects the differences in the user's response to different visual stimuli. It can characterize the physiological data sequence from the perspectives of time dimension, trend of change, or statistical characteristics. By performing correlation analysis between the first data feature and the display features of different depth layers, the physiological data sequence can be divided in the time dimension, thereby forming multiple physiological data subsequences, and each physiological data subsequence corresponds to a different depth layer.

[0051] For example, by analyzing the feature differences in physiological data sequences as the displayed content changes, and combining the display features of different depth layers, multiple continuous data segments can be identified in the physiological data sequences, and each data segment can be associated with a corresponding depth layer.

[0052] In operation S230, a target depth layer is determined based on the second data features of the physiological data subsequences corresponding to different depth layers. The second data features are used to further characterize the physiological data subsequences to reflect the user's overall physiological state or reaction level when viewing the corresponding depth layer. By comparing the second data features of the physiological data subsequences corresponding to different depth layers, the differences in the user's response to each depth layer can be assessed, and a target depth layer can be selected from multiple depth layers. For example, feature analysis is performed on the physiological data subsequences corresponding to multiple depth layers, and the analysis results are compared to select the depth layer that best matches the user's physiological response as the target depth layer.

[0053] Figure 3 Another flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0054] like Figure 3 As shown, based on the aforementioned embodiments, the depth adjustment method may further include operations S310~S320.

[0055] In operation S310, the original video corresponding to the target 3D video is acquired. Pixels in the original video contain depth information, and the pixels are divided into different depth layers based on this depth information. The original video refers to the 3D video source material before specific feature signals for inducing physiological feedback have been applied. The depth information it contains defines the positional layout of each pixel in the scene in 3D space. Dividing pixels into different depth layers means logically segmenting the continuous depth space into several independent pixel sets based on the distribution range of depth values. For example, acquiring a standard 3D movie source file as the original video, reading its depth channel data, and dividing the image pixels into three sets—foreground, midground, and background—according to a preset depth threshold.

[0056] In operation S320, different display adjustments are applied to pixels at different depth layers of the original video to obtain the target 3D video, so that when the target 3D video is displayed, pixels corresponding to different depth layers have different display characteristics. Display adjustment refers to modifying or modulating the image attributes or signal attributes of pixels in a specific mode. This adjustment aims to embed a unique "implicit tag" or "fingerprint" for each depth layer.

[0057] Different display adjustments are applied to pixels at different depth layers of the original video, with the degree of adjustment being less than the human eye's perception threshold for that adjustment. In other words, the adjustment intensity is typically controlled below the threshold of significant human perception, but sufficient to elicit differentiated responses from the nervous system. The resulting display characteristics are the physical signal properties exhibited by these adjustments during video playback.

[0058] By applying different parameters or types of display adjustments to different depth layers, the visually merged 3D image is endowed with structured differences at the signal level that can be resolved by physiological sensors (such as brain-computer interfaces). This provides a physical basis for subsequently establishing a mapping relationship between "depth layers and physiological signals".

[0059] For example, a high-frequency brightness adjustment imperceptible to the naked eye can be superimposed on the pixels of the near-field layer, and a slight chroma (such as saturation) shift can be superimposed on the pixels of the far-field layer; or, weak signal noise of different frequencies or modes can be superimposed on different depth layers respectively, so that each layer has different signal fluctuation characteristics when displayed.

[0060] According to embodiments of this disclosure, by introducing depth-layer-based differential display adjustments during the generation of a target 3D video from an original video, an "implicit index" is effectively established in the video content. This allows the user's physiological responses (such as brainwaves) used to watch the video to reverse-locate the depth layer of interest without requiring the user to wear additional eye-tracking hardware or undergo a special calibration process. This achieves an intelligent upgrade of existing 3D video technology in a low-invasive manner.

[0061] Figure 4A Another flowchart illustrating a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0062] like Figure 4A As shown, based on the aforementioned embodiments, operation S320 may include operation S410.

[0063] In operation S410, dynamic depth perturbations of different ranges are applied to pixels at different depth layers of the original video to obtain the target 3D video. When the target 3D video is displayed, the depth information of pixels at different depth layers changes dynamically within the corresponding range. Dynamic depth perturbation refers to superimposing a depth offset that changes continuously over time on top of the original static or motion depth information of the pixels. The magnitude of this offset change is usually set within a tiny range that is difficult for the human eye to perceive, but it is sufficient to induce a corresponding physiological response in the user's visual nervous system. Applying perturbations of different ranges means assigning unique perturbation parameters (such as amplitude ranges or change frequencies) to different depth layers, thereby giving different depth layers distinguishable physical identifiers in the time or frequency domain.

[0064] Through this processing, the depth or disparity values ​​of pixels belonging to different depth layers in the target 3D video are no longer constant or only change with the image content, but instead contain a preset dynamic fluctuation component. This fluctuation component acts as an implicit "depth watermark," encoding the identity information of the depth layer into the visual signal.

[0065] For example, combined with appendix Figure 1B The depth layer division shown represents high-frequency depth oscillations in layer 1 with pixel overlap ranging from [0, 0.1] pixels, and low-frequency depth oscillations in layer 2 with pixel overlap ranging from [0.2, 0.3] pixels. When a user views the image, although the perceived depth is stable, the minute in-and-out movements of pixels at different layers create differentiated stimulation patterns in the retina and visual cortex.

[0066] According to embodiments of this disclosure, by using differentiated dynamic depth perturbations as a means of distinguishing different depth layers, the problem of passively tracking the user's gaze depth in three-dimensional space can be effectively solved. Compared to marking methods that change color or brightness, depth dimension perturbations are more in line with the characteristics of three-dimensional visual perception. They can establish a high signal-to-noise ratio depth layer index in physiological signals without compromising the color fidelity of the image or interfering with the user's normal viewing experience (i.e., maintaining visual concealment), thereby providing a robust physical basis for subsequent accurate target depth layer identification.

[0067] Based on the aforementioned embodiments, the target duration is greater than or equal to a first duration threshold and less than a second duration threshold. The first duration threshold is greater than the time it takes for the human eye to observe different depth layers in a single observation, and the second duration threshold is less than the time it takes for the human eye to perceive the movement of an object.

[0068] The target duration is defined as the time window for collecting physiological data sequences; the first duration threshold represents the lower bound of the shortest time required to complete a "from surface to depth" deep scan; the second duration threshold represents the upper bound of the time when the user begins to recognize changes in the display as object movement. Setting the target duration between the two thresholds aims to ensure that the collection time window covers a complete deep scan process while avoiding the introduction of explicitly perceptible motion cues within that time window.

[0069] Specifically, when the target duration exceeds the first duration threshold, physiological data more fully reflects the user's comprehensive viewing response across multiple depth layers; when the target duration is less than the second duration threshold, changes in display related to depth layers are less likely to trigger the subjective feeling of "the screen is moving," thereby reducing interruptions to immersive viewing.

[0070] Figure 4B The diagram illustrates the relationship between target duration and depth layers according to embodiments of this disclosure. (See attached diagram.) Figure 4B As shown, the user's visual perception gradually transitions from layer 1 to layer 3, with time progressing from T0 to T3. T0 to T3 corresponds to one deep browsing process. The first duration threshold is higher than the duration from T0 to T3, so that the target duration covers this process. The second duration threshold is lower than the time scale at which the user perceives the movement of the object, so that the target duration falls within the range of "no significant perceived movement".

[0071] According to the embodiments of this disclosure, by constraining the time window as described above, the collected physiological data achieves a balance between representativeness and concealment, reducing the fluctuation in judgment caused by an excessively short time window, while suppressing the risk of discomfort caused by perceptible motion, thereby improving the problem of different experiences when different users watch 3D videos.

[0072] Figure 5A The flowchart illustrating the establishment of the correspondence between physiological data subsequences and depth layers in the depth adjustment method according to an embodiment of the present disclosure is shown in the diagram.

[0073] like Figure 5A As shown, based on the aforementioned embodiments, different display features correspond to different preset physiological data features. Different display features refer to the visual attributes exhibited by different depth layers in a 3D video, such as depth range, pixel density, and contrast. These attributes determine the differences in visual perception at different depth layers. Preset physiological data features refer to physiological response patterns predefined during the design phase and associated with these display features, such as the activity characteristics of different frequency bands in an electroencephalogram (EEG) signal, or changes in visual focus.

[0074] Figure 5B The illustration schematically shows the matching of depth layers with physiological data sequences according to embodiments of the present disclosure.

[0075] For example, Figure 5B The upper right corner schematically illustrates the correspondence between different display features and different preset physiological data features. Different textures or markers represent display features at different depths, while the different waveforms indicated by the arrows represent the corresponding preset physiological data features, illustrating the differentiated response patterns that different display features may trigger in physiological signals.

[0076] Operation S220 may include operations S510~S520.

[0077] In operation S510, the time domain range corresponding to different display features is determined based on the matching results of the first data features and the preset physiological data features. The first data features typically refer to the preliminary feature data collected from physiological signals such as electroencephalograms (EEGs) when the user watches a video, such as the amplitude or frequency distribution of EEG signals; the preset physiological data features are physiological response models corresponding to different display features, obtained through experiments or data analysis. By matching these data features, it can be determined which type of display feature can better match the user's physiological response within a certain time range.

[0078] For example, such as Figure 5B As shown, the physiological data sequence exhibits different waveform variation characteristics on the time axis from T0 to T3. By matching these variation characteristics with the preset physiological data characteristics shown in the upper right corner, it can be determined that T0 to T1, T1 to T2, and T2 to T3 correspond to different display characteristics.

[0079] In operating S520, continuous data sequences within different time domains are treated as physiological data subsequences and associated with different depth layers. A physiological data subsequence refers to a segment of physiological data collected continuously within the same time domain; therefore, this physiological data subsequence can be considered as the physiological response generated by the user when perceiving corresponding display features. By establishing a correspondence between different physiological data subsequences and depth layers with corresponding display features, changes in physiological data over time are mapped to the depth layer dimension, thus completing the construction of the association between physiological data and depth layers.

[0080] For example, combining Figure 5B Physiological data within the time interval T0 to T1 are used as the first physiological data subsequence and correspond to layer 1; physiological data within the time interval T1 to T2 are used as the second physiological data subsequence and correspond to layer 2; and physiological data within the time interval T2 to T3 are used as the third physiological data subsequence and correspond to layer 3.

[0081] According to embodiments of this disclosure, by setting corresponding preset physiological data features for different display features, and based on the matching results of the first data feature and the preset physiological data features, the time domain range corresponding to different display features is determined in the physiological data sequence, so that continuously collected physiological data can be naturally divided into multiple physiological data subsequences in the time dimension. Then, the physiological data subsequences are respectively associated with different depth layers, thereby avoiding the need for manual annotation or external synchronization between physiological data and depth layers, improving the accuracy and stability of establishing the correspondence between physiological data and depth layers, and providing a reliable data foundation for subsequent depth-based analysis and processing.

[0082] Figure 6 Another flowchart illustrating the determination of a target depth layer in a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0083] like Figure 6 As shown, based on the aforementioned embodiments, operation S230 may include operations S610 to S620.

[0084] In operation S610, based on the second data features of the physiological data subsequences corresponding to different depth layers, the physiological comfort evaluation results for users viewing pixels at different depth layers are determined. The second data features are characteristic information used to characterize the overall state of the physiological data subsequences, reflecting the user's physiological response level when viewing pixels at the corresponding depth layer. The physiological comfort evaluation results describe the objective physiological reflection of the user's subjective comfort level when viewing different depth layers. By extracting the second data features from the physiological data subsequences corresponding to different depth layers and analyzing these features, the physiological comfort evaluation results for users viewing each depth layer can be obtained, thus making different depth layers comparable in terms of comfort.

[0085] For example, for the physiological data subsequences corresponding to layers 1, 2 and 3 respectively, their second data features are extracted respectively, and the physiological comfort evaluation results of users when watching layers 1, 2 and 3 are obtained based on the second data features, so as to characterize the differences in users' physiological feelings to different depth layers.

[0086] In operation S620, a target depth layer is determined based on the physiological comfort evaluation results. The target depth layer refers to the depth layer among multiple depth layers whose corresponding physiological comfort evaluation results meet the expected conditions. The expected conditions can be used to characterize a physiological comfort level that is superior or more suitable for the user's current viewing state. For example, when there are differences in the physiological comfort evaluation results corresponding to multiple depth layers, the depth layer whose physiological comfort evaluation results better meet the expected conditions is selected as the target depth layer for subsequent depth display optimization.

[0087] According to embodiments of this disclosure, by introducing a comfort evaluation based on physiological data into the process of determining the target depth layer, the selection of the depth layer no longer depends solely on display parameters or preset rules, but can reflect the user's physiological state during actual viewing, thereby improving the consistency between the target depth layer determination result and the user's actual viewing experience, and helping to alleviate viewing discomfort caused by unreasonable depth settings.

[0088] Based on the foregoing embodiments, the second data feature includes at least one of the following: the variance of the physiological data subsequence, the energy intensity of the physiological data subsequence in the first band, the energy intensity of the physiological data subsequence in the second band, the energy intensity of the physiological data subsequence in the third band, and the rate of change of the physiological data subsequence; wherein, the first band is positively correlated with the user's level of concentration, the second band is negatively correlated with the user's level of concentration, and the third band is positively correlated with the user's level of fatigue and / or dizziness.

[0089] The variance in the second data feature reflects the degree of fluctuation of physiological signals over time. High variance typically indicates a higher visual fusion load, while low variance indicates a more stable visual fusion state. The first band (e.g., alpha wave) represents the user's level of focus, while the second band (e.g., beta wave) is negatively correlated with focus; increased beta wave power is usually associated with improved task focus. The third band (e.g., theta wave) is typically used to represent fatigue and dizziness; increased theta wave is associated with fatigue and discomfort caused by prolonged viewing or visual tasks. The rate of change reflects the drastic change in physiological signals; rapid and drastic changes usually indicate that the user is experiencing strong visual discomfort or fatigue. Combining the energy intensity, variance, and rate of change of each band can effectively assess the user's physiological comfort when viewing at different depths. For example, when a user watches 3D videos at different depths over a period of time, the system analyzes the EEG signal and calculates the variance, alpha wave power, beta wave power, theta wave power, and signal change rate at different time periods, thereby obtaining the physiological comfort characteristics corresponding to different depths (such as layer 1, layer 2, and layer 3).

[0090] For example, the i-th depth layer L i The corresponding physiological data subsequence is EEGi, and its variance characteristic F can be calculated. i =Var(EEG i ), calculate the β-band energy intensity Eng i =Power β (EEG i ), calculate the α-band energy intensity Att i =Power α (EEG i ), calculate the energy intensity D in the θ band. i =Powerθ (EEG i The rate of change feature Ui is calculated. Furthermore, these features can be fused to form the physiological comfort evaluation result for this depth layer, such as the physiological comfort evaluation result S. i It can be:

[0091] S i =ω f F i +ω e Eng i -ω a Att i -ω d D i -ω u U i

[0092] Where, ω f ω e ω a ω d ω u The preset weights are used to calculate the physiological comfort evaluation results for each depth, and the depth layer corresponding to the maximum value is selected as the target depth layer.

[0093] According to embodiments of this disclosure, by introducing second data features such as variance, energy intensity of different bands, and rate of change, the physiological comfort of users when viewing different depth layers can be objectively quantified. Thus, the determination of the target depth layer no longer depends on a single display parameter, but can comprehensively reflect the differences in user concentration and fatigue / dizziness response, which helps to improve the rationality and individual suitability of the target depth layer selection.

[0094] Figure 7 A flowchart illustrating the depth information of pixels in a whole target 3D video in a depth adjustment method according to an embodiment of the present disclosure is shown schematically.

[0095] like Figure 7 As shown, based on the aforementioned embodiments, operation S130 may include operation S710, and either operation S720 or operation S730.

[0096] In operation S710, based on the target depth layer and the depth information of the target of interest, an adjustment magnitude is determined. The adjustment magnitude is less than or equal to the depth distance between the corresponding pixel of the target of interest and the target depth layer. The adjustment magnitude characterizes the degree of adjustment made to the pixel in the depth direction, reflecting the amount of change between the pixel's original depth position and the target depth layer. The depth information of the target of interest indicates the current depth position of the target of interest, while the target depth layer indicates the depth position that the target of interest is expected to approach.

[0097] When operating the S720, the depth information of each pixel in the target 3D video is adjusted based on the adjustment range. In one adjustment method, the adjustment range is not only used to focus on the target itself, but also serves as a benchmark for overall depth adjustment, used to uniformly or correlate the depth information of each pixel in the target 3D video. For example, when it is determined that the target of interest needs to be moved a certain depth towards the target depth layer, this adjustment range is simultaneously applied to other pixels in the target 3D video, causing the overall depth structure of the entire image to shift towards the target depth layer, thereby maintaining spatial consistency in the image.

[0098] When operating the S730, the depth information of the corresponding pixels of the target in the target 3D video is adjusted based on the adjustment range.

[0099] In another adjustment method, depth adjustment can be performed only on the pixels corresponding to the target of interest, causing the target of interest to shift in the depth direction.

[0100] Building upon the previous adjustment method, the adjustment range is limited to no more than the depth distance between the pixel corresponding to the target and the target depth layer, thus preventing the target from being adjusted to a position beyond the target depth layer. For example, when the target is currently located in layer 3 and the target depth layer is layer 2, the adjustment range can be determined as the depth distance by which the target moves towards layer 2, and this adjustment range does not exceed the depth difference between layer 3 and layer 2, allowing the target to gradually approach the target depth layer without crossing the boundary.

[0101] According to embodiments of this disclosure, by introducing adjustment range control based on the target depth layer and the depth information of the target of interest during the depth adjustment process, the depth adjustment can be carried out around the target of the user's interest, while avoiding excessive depth adjustment that would destroy the original spatial hierarchy of the image. Thus, while highlighting the target of interest, the stability and consistency of the overall display of the 3D video are maintained.

[0102] Based on the foregoing embodiments, operation S710 may include: determining average depth information, where the average depth information is the average depth information of the pixels corresponding to the target of interest in the target 3D video; and determining the adjustment range based on the average depth information and the depth information of the target depth layer. The average depth information characterizes the overall depth position of the target of interest in 3D space, and is obtained by statistically analyzing the depth information of multiple pixels corresponding to the target of interest, thereby reducing the impact of depth anomalies of individual pixels on the adjustment result. The depth information of the target depth layer characterizes the reference position of the depth layer in the depth direction. By comparing the average depth information of the target of interest with the depth information of the target depth layer, the deviation between the two in the depth direction can be determined, and the adjustment range for depth adjustment can be determined based on this deviation. This method allows depth adjustment to be based on the overall depth position of the target of interest, rather than relying on the instantaneous depth value of a single pixel.

[0103] For example, let the set of pixels corresponding to the target of interest be O. focus Its average depth is D avg (O focus The center depth of the target depth layer is d. Lbest In one implementation, the adjustment magnitude ΔD can be determined based on the following relationship:

[0104] ΔD=α(d Lbest -D avg (O focus ))

[0105] Where α∈(0,1) is the smoothing coefficient, used to control the magnitude of the adjustment. When the average depth of the target of interest is greater than the depth of the target depth layer, the adjustment magnitude is negative, causing the target of interest to move closer to the target depth layer; when the average depth of the target of interest is less than the depth of the target depth layer, the adjustment magnitude is positive.

[0106] According to embodiments of this disclosure, by using the average depth information of the target of interest as a reference benchmark for depth adjustment, the depth adjustment process can reflect the overall positional characteristics of the target of interest in space, thereby avoiding the instability problem caused by depth fluctuations of individual pixels. Simultaneously, introducing a smoothing coefficient to constrain the adjustment range helps to achieve gradual depth adjustment, reducing the impact of abrupt depth changes on the viewing experience.

[0107] Building upon the aforementioned embodiments, the depth adjustment method further includes: adjusting the disparity data of the target 3D video based on the adjusted depth information. Disparity data characterizes the pixel displacement relationship between the left and right eye images, directly affecting the stereoscopic presentation effect of the 3D video. The adjusted depth information reflects the new position of pixels in the depth direction; therefore, the corresponding disparity data needs to be updated to maintain consistency between the depth information and the stereoscopic display. Adjusting the disparity data based on the adjusted depth information means that after a change in depth information, the relative displacement of corresponding pixels in the left and right eye images is synchronously corrected, matching the direction of disparity change with the direction of depth adjustment, thereby avoiding inconsistencies between the left and right eye displays after depth adjustment.

[0108] Based on the foregoing embodiments, operation S120 may include one or more of the following: determining the user's focus in the target 3D video based on the physiological data sequence; determining the user's focus in the target 3D video based on the user's posture data; and determining the user's focus based on the user's interaction behavior with the target 3D video.

[0109] Physiological data sequences, such as electroencephalogram (EEG) signals, are used to capture a user's physiological responses while watching 3D videos. By analyzing this physiological data, the degree of attention a user pays to different areas in the video can be determined, thus inferring their focus. Postural data reflects the user's viewing focus by detecting head or eye movements; combining this data allows for accurate identification of the user's focus. Interactive behaviors, including the areas or objects selected by the user when interacting with the 3D video through gestures, remote control operation, touchscreens, etc., can also serve as a basis for determining the focus.

[0110] Determining the focus of attention based on physiological data sequences primarily involves analyzing changes in attention-related bands (such as beta waves) in EEG signals to identify the user's concentration point. Determining the focus of attention based on posture data involves tracking the user's head or eye movements to infer their visual focus area. Determining the focus of attention based on interactive behavior is more direct; the user selects an object or area through interaction with the video content, and the system can immediately identify and set that object or area as the focus of attention.

[0111] For example, based on EEG data, by analyzing the user's electroencephalogram (such as beta waves) while watching videos, it can be found that the user's brain activity in a certain area is significantly enhanced, indicating that the area is the user's focus; based on posture data, if the user continuously focuses their gaze on the lower left corner of the screen, it can be determined that the area is their focus; based on interactive behavior, when the user selects a virtual object or clicks on a certain area with a remote control, the system sets that area as their focus.

[0112] According to embodiments of this disclosure, by combining different data sources (such as physiological data, posture data, and interaction behavior), it is possible to more accurately and comprehensively determine the user's focus target, thereby providing a more precise basis for subsequent operations such as depth adjustment and parallax update.

[0113] Figure 8 A block diagram of a depth adjustment device according to an embodiment of the present disclosure is shown schematically.

[0114] like Figure 8 As shown, the depth adjustment device 800 may include a first determining module 810, a second determining module 820, and a first adjusting module 830.

[0115] The first determining module 810 is used to determine the target depth layer during the user's viewing of the target 3D video. The target depth layer is determined based on the physiological data sequence generated within the target viewing time of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video. In some embodiments, the first determining module 810 can be used to perform operation S110 in the depth adjustment method described above, which will not be elaborated here.

[0116] The second determining module 820 is used to determine the user's current focus target while watching the target 3D video. In some embodiments, the second determining module 820 may be used to perform operation S120 in the depth adjustment method described above, which will not be elaborated here.

[0117] The first adjustment module 830 is used to adjust the depth information of pixels in the target 3D video based on the target depth layer and the target of interest. In some embodiments, the first adjustment module 830 can be used to perform operation S130 in the depth adjustment method described above, which will not be elaborated here.

[0118] According to embodiments of this disclosure, the first determining module may include a first acquiring module, a first corresponding module, and a third determining module.

[0119] The first acquisition module is used to acquire physiological data sequences generated during the target duration of a user's viewing of a target 3D video. Pixels in the target 3D video have depth information, and pixels are divided into different depth layers based on the depth information. Pixels in different depth layers have different display characteristics. In some embodiments, the first acquisition module can be used to perform operation S210 in the depth adjustment method described above, which will not be elaborated here.

[0120] The first correspondence module is used to establish correspondences between multiple physiological data subsequences in the physiological data sequence and different depth layers based on the first data features and display features of the physiological data sequence. In some embodiments, the first correspondence module can be used to perform operation S220 in the depth adjustment method described above, which will not be elaborated here.

[0121] The third determining module is used to determine the target depth layer based on the second data features of the physiological data subsequences corresponding to different depth layers. In some embodiments, the third determining module can be used to perform operation S230 in the depth adjustment method described above, which will not be elaborated here.

[0122] According to embodiments of this disclosure, the depth adjustment device may include a second acquisition module and a second adjustment module.

[0123] The second acquisition module is used to acquire the original video corresponding to the target 3D video. The pixels in the original video have depth information, and the pixels are divided into different depth layers according to the depth information. In some embodiments, the second acquisition module can be used to perform operation S310 in the depth adjustment method described above, which will not be elaborated here.

[0124] The second adjustment module is used to apply different display adjustments to pixels at different depth layers of the original video to obtain a target 3D video, so that when the target 3D video is displayed, pixels corresponding to different depth layers have different display characteristics. In some embodiments, the second adjustment module can be used to perform operation S320 in the depth adjustment method described above, which will not be elaborated here.

[0125] According to embodiments of this disclosure, the second adjustment module may include a third adjustment module.

[0126] The third adjustment module is used to apply dynamic depth perturbations of different ranges to pixels at different depth layers of the original video to obtain a target 3D video; when the target 3D video is displayed, the depth information of pixels at different depth layers changes dynamically within the corresponding range. In some embodiments, the third adjustment module can be used to perform operation S410 in the depth adjustment method described above, which will not be elaborated here.

[0127] According to embodiments of this disclosure, different display features correspond to different preset physiological data features, and the first data feature based on the physiological data sequence corresponds to the display feature. The first correspondence module may include a fourth determination module and a second correspondence module.

[0128] The fourth determining module is used to determine the time domain range corresponding to different display features based on the matching result between the first data feature and the preset physiological data feature. In some embodiments, the fourth determining module can be used to perform operation S510 in the depth adjustment method described above, which will not be elaborated here.

[0129] The second correspondence module is used to establish a correspondence between continuous data sequences in different time domain ranges as physiological data subsequences and different depth layers. In some embodiments, the second correspondence module can be used to perform operation S520 in the depth adjustment method described above, which will not be elaborated here.

[0130] According to embodiments of this disclosure, the first determining module may include a fifth determining module and a sixth determining module.

[0131] The fifth determining module is used to determine the physiological comfort evaluation result of the user viewing pixels at different depth layers based on the second data features of the physiological data subsequences corresponding to different depth layers. In some embodiments, the fifth determining module can be used to perform operation S610 in the depth adjustment method described above, which will not be elaborated here.

[0132] The sixth determining module is used to determine the target depth layer based on the physiological comfort evaluation results. In some embodiments, the sixth determining module can be used to perform operation S620 in the depth adjustment method described above, which will not be elaborated here.

[0133] According to embodiments of this disclosure, the first adjustment module may include a seventh determining module, a fourth adjustment module, and a fifth adjustment module.

[0134] The seventh determining module is used to determine an adjustment range based on the depth information of the target depth layer and the target of interest, wherein the adjustment range is less than or equal to the depth distance between the pixel corresponding to the target of interest and the target depth layer. In some embodiments, the seventh determining module may be used to perform operation S710 in the depth adjustment method described above, which will not be elaborated here.

[0135] The fourth adjustment module is used to adjust the depth information of each pixel in the target 3D video based on the adjustment range. In some embodiments, the fourth adjustment module can be used to perform operation S720 in the depth adjustment method described above, which will not be elaborated here.

[0136] The fifth adjustment module is used to adjust the depth information of the pixels corresponding to the target of interest in the target 3D video based on the adjustment range. In some embodiments, the fifth adjustment module can be used to perform operation S730 in the depth adjustment method described above, which will not be elaborated here.

[0137] Any one or more of the modules, submodules, units, and subunits according to embodiments of this disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of this disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of this disclosure can be at least partially implemented as hardware circuitry, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of this disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0138] For example, any plurality of the first determining module 810, the second determining module 820, and the first adjusting module 830 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this disclosure, at least one of the first determining module 810, the second determining module 820, and the first adjusting module 830 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first determining module 810, the second determining module 820, and the first adjusting module 830 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0139] It should be noted that the data processing system part in the embodiments of this disclosure corresponds to the data processing method part in the embodiments of this disclosure. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0140] Figure 9 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0141] like Figure 9 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0142] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0143] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0144] According to embodiments of this disclosure, the method flow according to embodiments of this disclosure can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by processor 901, it performs the functions defined in the system of embodiments of this disclosure. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0145] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0146] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0147] For example, according to embodiments of this disclosure, a computer-readable storage medium may include one or more memories other than ROM 902 and / or RAM 903 described above.

[0148] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the depth adjustment method provided in the embodiments of this disclosure.

[0149] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0150] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices or magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 909, and / or installed from removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof. According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0151] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0152] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A depth adjustment method, comprising: During the user's viewing of the target 3D video, the target depth layer is determined. The target depth layer is determined based on the physiological data sequence generated within the target viewing time of the target 3D video and the display characteristics of pixels at different depth layers in the target 3D video. Determine the user's current focus while watching the target 3D video; Based on the target depth layer and the target of interest, the depth information of pixels in the target 3D video is adjusted.

2. The method according to claim 1, wherein determining the target depth layer comprises: Acquire the physiological data sequence generated by the user during the target duration of watching the target 3D video. The pixels in the target 3D video have depth information, and the pixels are divided into different depth layers according to the depth information. Pixels in different depth layers have different display characteristics. Based on the first data feature and the display feature of the physiological data sequence, a correspondence is established between multiple physiological data subsequences in the physiological data sequence and different depth layers. The target depth layer is determined based on the second data features of the physiological data subsequences corresponding to different depth layers.

3. The method according to claim 2, further comprising: Obtain the original video corresponding to the target 3D video, wherein the pixels in the original video have depth information and the pixels are divided into different depth layers according to the depth information; Different display adjustments are applied to pixels at different depth layers of the original video to obtain a target 3D video, so that when the target 3D video is displayed, pixels at different depth layers have different display characteristics.

4. The method according to claim 3, wherein applying different display adjustments to pixels at different depth layers of the original video to obtain the target 3D video comprises: By applying dynamic depth perturbations of different ranges to pixels at different depth layers of the original video, a target 3D video is obtained. When the target 3D video is displayed, the depth information of pixels at different depth layers changes dynamically within the corresponding range.

5. The method according to claim 1, wherein the target duration is greater than a first duration threshold and less than a second duration threshold, the first duration threshold is greater than the time it takes for the human eye to observe different depth layers in a single observation, and the second duration threshold is less than the time it takes for the human eye to perceive the movement of an object.

6. The method according to claim 2, wherein different display features correspond to different preset physiological data features, and the step of establishing a correspondence between multiple physiological data subsequences in the physiological data sequence and different depth layers based on the first data feature of the physiological data sequence and the display features includes: Based on the matching result between the first data feature and the preset physiological data feature, the time domain range corresponding to different display features is determined; Continuous data sequences within different time domains are used as physiological data subsequences, and a correspondence is established with different depth layers.

7. The method according to claim 2, wherein determining the target depth layer based on the second data features of physiological data subsequences corresponding to different depth layers includes: Based on the second data features of the physiological data subsequences corresponding to different depth layers, the physiological comfort evaluation results of users viewing pixels at different depth layers are determined. Based on the physiological comfort evaluation results, the target depth layer is determined.

8. The method according to claim 7, wherein the second data feature includes at least one of the following: the variance of the physiological data subsequence, the energy intensity of the physiological data subsequence in a first band, the energy intensity of the physiological data subsequence in a second band, the energy intensity of the physiological data subsequence in a third band, and the rate of change of the physiological data subsequence; in, The first band is positively correlated with the user's level of concentration, the second band is negatively correlated with the user's level of concentration, and the third band is positively correlated with the user's level of fatigue and / or dizziness.

9. The method according to claim 1, wherein adjusting the depth information of pixels in the target 3D video based on the target depth layer and the target of interest comprises: Based on the depth information of the target depth layer and the target of interest, an adjustment range is determined, wherein the adjustment range is less than or equal to the depth distance between the pixel corresponding to the target of interest and the target depth layer; Based on the adjustment range, the depth information of each pixel in the target 3D video is adjusted; or, Based on the adjustment range, the depth information of the corresponding pixels of the target of interest in the target 3D video is adjusted.

10. An electronic device, comprising: At least one memory for storing computer programs; At least one processor; The computer program is used to execute the computer program to perform at least one of the following operations: during the user's viewing of the target 3D video, determining the target depth layer, the target depth layer being determined based on the physiological data sequence generated within the target duration of the user's viewing of the target 3D video, and the display characteristics of pixels at different depth layers in the target 3D video; Determine the target of interest that the user is currently watching in the target 3D video; based on the target depth layer and the target of interest, adjust the depth information of pixels in the target 3D video.

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