Display screen double-perception image quality optimization method, device and system and storage medium

Abstract

The invention relates to a display screen double-perception image quality optimization method, system and device and a storage medium. The method comprises the following steps: acquiring a current ambient light parameter of a display screen and to-be-displayed frame image data; analyzing and classifying the current ambient light parameter to obtain a scene mode code, and performing multi-dimensional feature extraction on the to-be-displayed frame image data to generate content perception metadata; respectively carrying out decision calculation on the scene mode code and the content perception metadata to obtain a pixel brightness adjustment signal and partition backlight control data; and performing fusion adjustment on the to-be-displayed frame image data according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data. According to the method, the overall picture brightness can be dynamically optimized according to the environment illumination condition, and meanwhile, the local contrast and details are specifically enhanced according to the brightness distribution and color characteristics of the image content.

Description

Technical Field

[0001] This invention relates to the technical field of display screens, and in particular to a method, apparatus, system, and storage medium for dual-sensor image quality optimization of a display screen. Background Technology

[0002] With the rapid development of display technology and the increasing demands of users for visual experience, display image quality optimization has become crucial for enhancing the competitiveness of consumer electronics products. Current display image quality optimization technologies typically handle ambient light adaptation or image content enhancement independently: ambient light adaptation solutions primarily adjust the overall backlight based on a single dimension of ambient light intensity, making it difficult to handle the fine contrast requirements of complex image content (such as the coexistence of local highlights and shadows); while content-based image enhancement algorithms focus mainly on enhancing the features of the image itself, failing to dynamically adjust in conjunction with real-time ambient light conditions, resulting in insufficient visibility in bright light environments or excessive power consumption in low light environments. This approach, which separates scene perception from content perception, prevents image quality optimization strategies from achieving global optimization. Summary of the Invention

[0003] The main objective of this invention is to provide a dual-sensor image quality optimization method, device, system, and storage medium for a display screen. This method can dynamically optimize the overall screen brightness based on ambient lighting conditions, while also enhancing local contrast and details based on the brightness distribution and color characteristics of the image content itself. This ensures excellent visibility and image quality reproduction under various ambient lighting conditions.

[0004] To achieve the above objectives, the present invention provides a dual-sensor image quality optimization method for a display screen, comprising: Obtain the current ambient light parameters of the display screen and the image data of the frame to be displayed; The current ambient light parameters are analyzed and classified to obtain scene mode encoding, and multi-dimensional feature extraction is performed on the image data of the frame to be displayed to generate content-aware metadata. Decision-making calculations are performed on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data; The image data of the frame to be displayed is fused and adjusted according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

[0005] Furthermore, obtaining the current ambient light parameters of the display screen and the image data of the frame to be displayed includes: The raw light signal collected by the integrated light sensor of the display screen is acquired, and the raw light signal is converted into the current ambient light parameters; Extract the current frame image data to be displayed from the graphics processing line of the display screen, and compare the current frame image data to be displayed with the previous frame image data to be displayed; If the difference between the current frame image data to be displayed and the previous frame image data to be displayed exceeds a preset display threshold, then the frame image data to be displayed is determined to be valid; otherwise, the frame image data to be displayed from the previous frame is reused.

[0006] Furthermore, the process of parsing and classifying the current ambient light parameters to obtain scene mode encoding, and extracting multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata, includes: Based on the light intensity rules of the preset environment mapping table, the current ambient light parameters are used to perform light identification to obtain the light intensity level code; The current ambient light parameters are color temperature identified according to the color temperature rules of the environment mapping table to obtain a color temperature label. The light intensity level code and the color temperature label are then integrated to obtain the scene mode code. Brightness analysis is performed on the image data of the frame to be displayed to obtain brightness distribution characteristic data; Color feature data is obtained by performing color statistics on the image data of the frame to be displayed based on the brightness distribution feature data. The brightness distribution feature data and the color feature data are integrated to obtain the content-aware metadata.

[0007] Further, the step of performing brightness analysis on the image data to be displayed to obtain brightness distribution feature data includes: The image data of the frame to be displayed is divided into multiple image partitions according to the preset partition grid rules. The pixel brightness of each image partition is calculated in turn to obtain the partition brightness value. Each partition brightness value is compared with a first brightness threshold of the preset partition grid rule. If the partition brightness value is higher than the first brightness threshold, the partition brightness value is marked as a high-brightness partition. Each partition brightness value is compared with a second brightness threshold of the preset partition grid pattern. If the partition brightness value is lower than the second brightness threshold, the partition brightness value is marked as a dark partition. By integrating all the aforementioned partition brightness values, partition brightness data is obtained. The brightness distribution feature data is obtained by integrating the partition brightness data, the bright partition, the dark partition, and the image partition.

[0008] Further, the decision-making calculations performed on the scene mode encoding and the content-aware metadata to obtain pixel brightness adjustment signals and zone backlight control data include: Based on the scene mode code, the preset scene strategy mapping table is queried to obtain the corresponding basic backlight brightness value and dynamic enhancement coefficient; Based on the content-aware metadata, a global baseline calculation is performed on the basic backlight brightness value to obtain a global backlight baseline value. Based on the global backlight reference value, the content-aware metadata is calibrated for partitioned brightness to obtain the partitioned backlight control data. The pixel brightness of the content-aware metadata is adjusted by coefficients based on the global backlight reference value to obtain the initial brightness adjustment coefficients; The initial brightness adjustment coefficient and the dynamic enhancement coefficient are iteratively fused to obtain the pixel brightness adjustment signal.

[0009] Further, the step of performing partitioned brightness calibration on the content-aware metadata based on the global backlight reference value to obtain the partitioned backlight control data includes: Extract the partition brightness value of each image partition from the content-aware metadata, and calculate the ratio between each partition brightness value and the global backlight reference value in turn to obtain the corresponding partition ratio. Each of the partition ratios is compared sequentially with a preset backlight enhancement threshold; If the partition ratio is higher than the backlight enhancement threshold, then an enhanced brightness value is assigned to the corresponding image partition, and first allocation information is generated; If the partition ratio is not higher than the backlight enhancement threshold, then the global backlight reference value is assigned to the corresponding image partition, and second allocation information is generated; According to the preset control rules, all the first allocation information and the second allocation information are structurally integrated to obtain the partition backlight control data.

[0010] Further, the step of fusing and adjusting the image data of the frame to be displayed based on the partition backlight control data and the pixel brightness adjustment signal to obtain image display data includes: The partitioned backlight control data is matched with each image pixel in the frame image data to be displayed to obtain the backlight partition and backlight compensation coefficient of each image pixel; The original luminance component of each image pixel is obtained, and the original luminance component, the corresponding backlight zone, and the backlight compensation coefficient are initially adjusted to obtain the intermediate luminance component. Each intermediate brightness component is compared with a preset display brightness threshold. If it exceeds the display brightness threshold, the final brightness component of each image pixel is set to the display brightness threshold. Otherwise, the intermediate brightness component is set to the final brightness component. The final luminance component of each image pixel is converted and combined with the original chrominance component to obtain the image display data.

[0011] The present invention also provides a display screen dual-sensor image quality optimization device, applied to the display screen dual-sensor image quality optimization method described in any one of the above claims, comprising: The acquisition module is used to acquire the current ambient light parameters of the display screen and the image data of the frame to be displayed; The analysis module is used to analyze and classify the current ambient light parameters to obtain scene mode encoding, and to extract multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata. The association module is used to perform decision calculations on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data. The processing module is used to fuse and adjust the image data of the frame to be displayed according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

[0012] The present invention also provides a dual-sensor image quality optimization system for a display screen, comprising: Memory, used to store programs; A processor is used to execute the program to implement each step of the display screen dual-sensor image quality optimization method described in any of the above-mentioned embodiments.

[0013] The present invention also provides a storage medium storing computer instructions for causing a computer to perform any of the methods described above.

[0014] The present invention provides a dual-sensor image quality optimization method, apparatus, system, and storage medium for display screens, which have the following beneficial effects: By acquiring ambient light parameters and image data in parallel and collaboratively calculating and generating pixel-level brightness adjustment and zoned backlight control signals, a deep fusion of scene perception and content perception is achieved. It can dynamically optimize the overall screen brightness based on ambient lighting conditions, while simultaneously enhancing local contrast and detail according to the brightness distribution and color characteristics of the image content itself, thus maintaining excellent visibility and image quality reproduction under various ambient lighting conditions. By matching and calibrating the global backlight benchmark with image zone features, it achieves refined on-demand driving of the backlight system, avoiding the energy waste of traditional global dimming in complex content scenarios. Through pixel-level fusion adjustment, it outputs image display data that perfectly matches the perception decisions, ensuring end-to-end collaborative optimization from environmental perception and content analysis to hardware driving, significantly improving the overall energy efficiency of the display while enhancing the visual experience. Attached Figure Description

[0015] Figure 1 This is a flowchart of a dual-sensor image quality optimization method for a display screen provided by the present invention; Figure 2 This is a structural diagram of a dual-sensor image quality optimization device for a display screen provided by the present invention; Figure 3 This is a structural diagram of a dual-sensor image quality optimization system for a display screen provided by the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0019] Reference Figure 1 As shown, the present invention provides a dual-sensor image quality optimization method for a display screen, comprising: Step S1: Obtain the current ambient light parameters of the display screen and the image data of the frame to be displayed; Specifically, the current ambient light parameters are acquired through an ambient light sensor integrated into the display screen. This sensor continuously captures raw light signals from the environment, including illuminance and color temperature information. The raw light signals undergo analog-to-digital conversion and standardization calibration to quantize them into structured current ambient light parameters, including ambient illuminance and color temperature values. The image data to be displayed is acquired directly from the frame buffer of the graphics processor or display controller. A complete array of image data representing the image to be displayed is read from the frame buffer. The currently read image data is compared with the previously processed image data at the pixel level or feature level. This comparison generates a difference metric. If this metric exceeds a preset display threshold, the current frame is determined to be image data of a frame with valid content updates, and subsequent optimization processes are initiated; if it does not exceed the threshold, the image content is considered essentially static, and the optimization parameters generated for the previous frame can be reused, thereby reducing system power consumption.

[0020] Step S2: Analyze and classify the current ambient light parameters to obtain scene mode encoding, and extract multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata; Specifically, a pre-defined environment mapping table defines illuminance level codes corresponding to different illuminance ranges and color temperature labels corresponding to different color temperature ranges. The environment mapping table also contains various rules that match the illuminance and color temperature values ​​in the current ambient light parameters with threshold ranges in the mapping table to determine the specific illuminance level code and color temperature label. These two identifiers are then integrated according to a predetermined format to generate a composite scene mode code. This code uniquely represents the current physical environment state, such as "high illuminance cool color environment" or "low illuminance warm color environment".

[0021] In parallel threads, multi-dimensional feature extraction of the image data to be displayed is carried out simultaneously. Spatial structure analysis is performed on the image data, dividing it into multiple logical image partitions according to a pre-defined grid. Statistical features of each partition, such as average brightness and brightness variance, are calculated to identify significant bright and dark areas, forming spatial brightness distribution characteristics. Simultaneously, statistical calculations are performed on the overall image to obtain global average brightness, global brightness histogram, and the distribution and saturation information of major colors. Furthermore, pre-defined detection logic identifies key regions in the image, such as the location of face or text regions. All these feature data extracted from different dimensions are structured, managed, and integrated to constitute content-aware metadata. This metadata quantitatively describes the content characteristics of the current frame image from multiple levels, including brightness distribution, color composition, spatial structure, and semantic emphasis.

[0022] Step S3: Perform decision calculations on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data; Specifically, by querying a preset scene strategy mapping table, the basic control parameters bound to this specific environment mode are obtained, including a basic backlight brightness value and a dynamic enhancement coefficient. The basic backlight brightness value reflects the initial backlight requirements in this environment, while the dynamic enhancement coefficient defines the intensity tone of the image contrast adjustment.

[0023] Using global statistical information such as average image brightness from content-aware metadata, a global backlight baseline value is generated by correcting the base backlight brightness value through a preset calculation rule. Based on this global backlight baseline value, the brightness characteristics of each image partition recorded in the content-aware metadata are calibrated. The ratio of the brightness characteristic value (such as average brightness or peak brightness) of each partition to this global baseline value is calculated and compared with a backlight enhancement threshold. If the ratio of a partition exceeds the threshold, it is determined that the content in that area needs to be highlighted, and an enhanced brightness value higher than the global baseline is assigned to it; otherwise, the global baseline value itself is assigned. The allocation results of all partitions are integrated in spatial order to form the partition backlight control data that precisely controls each backlight unit.

[0024] At the pixel level, based on the global backlight baseline and finer-grained pixel-level brightness distribution in content-aware metadata, an initial brightness adjustment coefficient is calculated for each pixel to initially map the contrast within the image. This initial coefficient is then iteratively fused with dynamic enhancement coefficients obtained from the scene policy mapping table. The iterative process continuously performs weighted calculations and comparisons until the difference between two adjacent calculations is less than a preset convergence threshold. Finally, a pixel brightness adjustment signal is output.

[0025] Step S4: The image data of the frame to be displayed is fused and adjusted according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

[0026] Specifically, the backlight partition grid defined by the partition backlight control data is aligned with the pixel matrix in the image data of the frame to be displayed, thereby determining the specific backlight partition to which each pixel belongs and its corresponding backlight compensation coefficient.

[0027] For each pixel, its original luminance component is extracted and multiplied by the adjustment coefficient provided for that pixel position in the pixel luminance adjustment signal to obtain an intermediate result. This intermediate result is then multiplied by the backlight compensation coefficient of the backlight zone to which the pixel belongs, thereby superimposing the effect of zone backlight control onto the pixel data. The fused luminance value is compared with a preset display luminance threshold. If it exceeds the threshold, the final output luminance component of the pixel is clamped to the threshold; otherwise, the calculated value is retained as the final luminance component.

[0028] The final luminance component is recombined with the original chrominance component of the image data to be displayed to form complete optimized image data. This optimized image data is then converted into a specific electrical signal format required by the display driver chip, such as low-voltage differential signal or specially coded pixel data packets. This signal, after end-to-end optimization, is the final image display data.

[0029] This invention provides a dual-sensor image quality optimization method for displays. By simultaneously acquiring ambient light parameters and image data, and collaboratively calculating and generating pixel-level brightness adjustment and zoned backlight control signals, it achieves a deep fusion of scene perception and content perception. It can dynamically optimize the overall screen brightness based on ambient lighting conditions, while simultaneously enhancing local contrast and detail according to the brightness distribution and color characteristics of the image content itself, thus maintaining excellent visibility and image quality reproduction under various ambient lighting conditions. By matching and calibrating the global backlight benchmark with image zone features, it achieves refined on-demand driving of the backlight system, avoiding the energy waste of traditional global dimming in complex content scenarios. Through pixel-level fusion adjustment, it outputs image display data that perfectly matches the perception decisions, ensuring end-to-end collaborative optimization from environmental perception and content analysis to hardware driving, significantly improving the overall energy efficiency of the display while enhancing the visual experience.

[0030] In one embodiment, obtaining the current ambient light parameters of the display screen and the image data of the frame to be displayed includes: The raw light signal collected by the integrated light sensor of the display screen is acquired, and the raw light signal is converted into the current ambient light parameters; Extract the current frame image data to be displayed from the graphics processing line of the display screen, and compare the current frame image data to be displayed with the previous frame image data to be displayed; The graphics processing line refers to the data channel at the end of the graphics processor rendering pipeline that connects to the display controller or frame buffer, carrying the complete frame image data stream that has been rendered.

[0031] Specifically, the image capture module monitors the data flow on the graphics processing line and triggers a read operation of the current complete frame data during vertical blanking or at specific moments when the frame buffer switches. The source data read is located in the frame buffer in the system main memory or video memory, and the data format is RGB, YUV, or other standard pixel formats. The pixel data of the current frame is copied to the image processing buffer allocated specifically for the image quality optimization process. At the same time, the image data of the previous frame stored in another buffer is retrieved for pixel-by-pixel or feature block-based comparison calculations with the current frame data. The mean of the absolute values ​​of the differences between the brightness components of corresponding pixels in the two frames is calculated, or the structured similarity index between the two downsampled images is calculated, ultimately generating a scalar value representing the inter-frame difference. This difference value is compared with a preset display threshold. The entire comparison and determination process is designed to be completed within the time constraint of the frame period, and its output is a binary determination flag. This flag controls the selection of data streams: if the flag indicates a significant difference, the currently captured data is marked as valid and passed to the subsequent feature extraction process; otherwise, a reuse mechanism will be triggered, and subsequent processes will no longer repeat calculations based on new image data, but will instead use the content-aware metadata and optimization parameters generated for the previous frame.

[0032] If the difference between the current frame image data to be displayed and the previous frame image data to be displayed exceeds a preset display threshold, then the frame image data to be displayed is determined to be valid; otherwise, the frame image data to be displayed from the previous frame is reused.

[0033] The method provided in this embodiment achieves deep collaboration between scene perception and content perception by acquiring ambient light parameters and image data in parallel and performing analysis and feature extraction separately. This overcomes the problem in traditional solutions where the processing of these two aspects is fragmented, leading to a trade-off between image quality and energy efficiency. This ensures that the image maintains excellent visibility and realistic content reproduction under any lighting conditions. By introducing an inter-frame difference comparison and parameter reuse mechanism, full-link optimization calculations are triggered only when the image content is effectively updated, significantly reducing the average computational load on the processor. By collaboratively generating zoned backlight control data and pixel brightness adjustment signals based on the fused perception results and performing precise fusion adjustments before driving the display, the method achieves a unified approach to global backlight environmental adaptation, local backlight detail enhancement, and pixel-level contrast optimization.

[0034] In one embodiment, the process of parsing and classifying the current ambient light parameters to obtain scene mode encoding, and extracting multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata, includes: Based on the light intensity rules of the preset environment mapping table, the current ambient light parameters are used to perform light identification to obtain the light intensity level code; Specifically, the standardized current ambient light parameters provided by the ambient light parameter acquisition step are read from shared memory, and a verified ambient illuminance value is extracted from them. This illuminance value first undergoes a validity check to confirm that it is within the sensor's measurement range and is not an anomaly. This value is then input into the illuminance intensity query module of the environment mapping table, which performs a sequential comparison or binary search against multiple predefined, ascending-ordered illuminance threshold intervals in the mapping table. Each threshold interval is associated with a unique illuminance level code. The comparison logic determines which specific interval the illuminance value falls into. Once a match is successful, the query module outputs the illuminance level code associated with that interval.

[0035] The current ambient light parameters are color temperature identified according to the color temperature rules of the environment mapping table to obtain a color temperature label. The light intensity level code and the color temperature label are then integrated to obtain the scene mode code. Brightness analysis is performed on the image data of the frame to be displayed to obtain brightness distribution characteristic data; Color feature data is obtained by performing color statistics on the image data of the frame to be displayed based on the brightness distribution feature data. Specifically, the chromaticity component matrix corresponding to the luminance component is extracted from the image data of the frame to be displayed. Color statistics are performed in two parallel paths. The first path performs global color analysis: calculating the average saturation of the entire frame image and generating a two-dimensional distribution histogram of the main colors in the color gamut to identify the dominant color tone and its proportion. The second path performs region-based focused color analysis: this analysis utilizes the marked highlight and shadow regions information in the luminance distribution feature data. For each marked highlight region, the average saturation and representative hue value within the region are calculated, and the color characteristics of the highlight region are recorded to obtain the highlight region color set; similarly, for each marked shadow region, the average saturation within the region is calculated, and the color characteristics of the shadow region are recorded to obtain the shadow region color set. If the luminance distribution feature data contains the coordinates of key display areas identified by preset rules (such as face detection), the color features of these specific areas are additionally calculated to obtain the key region color set. Finally, the highlight region color set, shadow region color set, and key region color set are summarized and structurally encapsulated to form a color feature data object.

[0036] The brightness distribution feature data and the color feature data are integrated to obtain the content-aware metadata.

[0037] The method provided in this embodiment rapidly converts continuous ambient light parameters into discrete scene mode codes based on a predefined environment mapping table. Combined with gridded partitioning and brightness statistical analysis of image data, it achieves efficient and accurate perception of environmental conditions and image content. By performing parallel brightness distribution analysis and color statistics on image data and integrating multi-dimensional features into standardized content-aware metadata, it achieves a comprehensive quantitative description of image contrast, local highlights and shadows, and color composition, enabling optimization strategies to more accurately match the characteristics of the image itself. By collaboratively deciding and solving scene mode codes and content-aware metadata, it generates collaborative pixel-level brightness adjustment signals and partitioned backlight control data, achieving a deep fusion of environmental adaptation and content enhancement.

[0038] In one embodiment, the step of performing brightness analysis on the image data to be displayed to obtain brightness distribution feature data includes: The image data of the frame to be displayed is divided into multiple image partitions according to the preset partition grid rules. The pixel brightness of each image partition is calculated in turn to obtain the partition brightness value. Specifically, the complete luminance component matrix of the frame image data to be displayed is read. The size of this matrix is ​​consistent with the physical resolution of the display screen. A partition index mapping table is generated according to the pre-loaded partition grid rules. This mapping table defines the partition number to which each pixel coordinate in the original image belongs. This is achieved by comparing the pixel coordinates with the boundary coordinates of each partition defined in the rules, so that each pixel is uniquely assigned to a partition. For each partition, an independent calculation unit is started to sequentially traverse all pixels within that partition. The calculation unit accumulates the luminance component values ​​of all pixels in that partition and records the total number of valid pixels contained in that partition. After accumulation, the sum is divided by the total number of pixels, and a division operation is performed to obtain the average luminance value of that partition, i.e., the partition luminance value. Each calculated partition luminance value is bound to its corresponding partition number (including row and column numbers) to form a partition-luminance key-value pair. This key-value pair is temporarily stored in a temporary array or list, and its arrangement order is consistent with the partition index order.

[0039] Each partition brightness value is compared with a first brightness threshold of the preset partition grid rule. If the partition brightness value is higher than the first brightness threshold, the partition brightness value is marked as a high-brightness partition. Each partition brightness value is compared with a second brightness threshold of the preset partition grid pattern. If the partition brightness value is lower than the second brightness threshold, the partition brightness value is marked as a dark partition. By integrating all the aforementioned partition brightness values, partition brightness data is obtained. The brightness distribution feature data is obtained by integrating the partition brightness data, the bright partition, the dark partition, and the image partition.

[0040] The method provided in this embodiment divides the image according to a partitioned grid and calculates the brightness values ​​of each partition, converting high-resolution pixel data into more efficient statistical feature data, thus laying a low-computational-complexity data foundation for subsequent real-time image quality optimization. By independently comparing each partition brightness value with preset first and second brightness thresholds and marking bright and dark partitions respectively, rapid localization of bright and dark areas in the image is achieved. By integrating all partition brightness values ​​into partition brightness data that preserves spatial relationships and structurally fusing it with the marked bright and dark partition information, unified brightness distribution feature data is generated. This provides a multi-dimensional brightness description that combines complete field information and semantic emphasis, enabling subsequent color analysis and dual-sensory decision-making to be based on richer and more accurate content features, thereby improving the precision and adaptability of the overall image quality optimization effect.

[0041] In one embodiment, the decision-making process for the scene mode encoding and the content-aware metadata to obtain pixel brightness adjustment signals and zone backlight control data includes: Based on the scene mode code, the preset scene strategy mapping table is queried to obtain the corresponding basic backlight brightness value and dynamic enhancement coefficient; The pre-defined scene strategy mapping table predefines a set of matching image quality optimization strategy parameters for each discrete scene mode encoding. The dynamic enhancement coefficient is a numerical parameter used to control the intensity range of subsequent pixel-level brightness adjustments and the aggressiveness of contrast enhancement; its value range is calibrated to avoid visual artifacts.

[0042] Based on the content-aware metadata, a global baseline calculation is performed on the basic backlight brightness value to obtain a global backlight baseline value. Specifically, a global average brightness value is extracted from content-aware metadata. A predefined global baseline calculation rule is applied, which defines how to correct the base backlight brightness value based on the image's average brightness.

[0043] The average image brightness is compared to a standard mid-gray reference value calculated using a global baseline calculation rule, and a brightness scaling factor is calculated. If the average image brightness is significantly higher than the standard mid-gray, a decay factor less than 1 is generated to moderately reduce the base backlight brightness value; if the average image brightness is significantly lower than the standard mid-gray, an enhancement factor greater than 1 is generated to moderately increase the base backlight brightness value. The calculation rule includes a non-linear mapping or piecewise linear function, and has upper and lower limits for the correction amount. Finally, the base backlight brightness value is multiplied by the scaling factor, and the result is rounded and range-corrected to generate the global backlight baseline value.

[0044] Based on the global backlight reference value, the content-aware metadata is calibrated for partitioned brightness to obtain the partitioned backlight control data. The pixel brightness of the content-aware metadata is adjusted by coefficients based on the global backlight reference value to obtain the initial brightness adjustment coefficients; Specifically, the raw luminance component value of each pixel is read from the raw image data buffer associated with content-aware metadata. The adjustment process operates independently on each pixel. For the current pixel, its raw luminance component value is associated with the global backlight reference value. A normalized mapping is established. This mapping maps the input pair of raw luminance value - global backlight reference value to an adjustment coefficient between 0 and 1 (or other calibrated range). For example, the quotient of the raw luminance component value and the global backlight reference value is calculated, and this quotient is converted to output the corresponding adjustment coefficient. For dark pixels far below the reference value, the coefficient approaches 0; for mid-tone pixels close to the reference value, the coefficient is around 0.5; for bright pixels above the reference value, the coefficient approaches 1 or higher. This process iterates through all pixels in the image and integrates all adjustment coefficients to generate an initial luminance adjustment coefficient.

[0045] The initial brightness adjustment coefficient and the dynamic enhancement coefficient are iteratively fused to obtain the pixel brightness adjustment signal.

[0046] The method provided in this embodiment quickly converts the environmental scene code into corresponding basic backlight parameters and dynamic enhancement coefficients by querying a preset strategy mapping table, providing an accurate environmental benchmark for optimization. Based on the average image brightness in the content-aware metadata, this benchmark value is corrected to generate a global backlight benchmark value that integrates environmental and content features, achieving content-adaptive backlight anchoring. Using this benchmark value as a reference, the brightness ratio of each zone is calculated and compared with a threshold to allocate enhanced brightness to the core high-brightness area, generating zoned backlight control data. This achieves spatial coordination between backlight and image content, improving local contrast while saving energy. The original pixel brightness is normalized to an initial adjustment coefficient based on this benchmark and iteratively fused with the dynamic enhancement coefficient to obtain the final pixel brightness adjustment signal, achieving stable and precise pixel-level contrast enhancement and avoiding visual defects.

[0047] In one embodiment, the step of performing partitioned brightness calibration on the content-aware metadata based on the global backlight reference value to obtain the partitioned backlight control data includes: Extract the partition brightness value of each image partition from the content-aware metadata, and calculate the ratio between each partition brightness value and the global backlight reference value in turn to obtain the corresponding partition ratio. Specifically, based on the grid definition described in the content-aware metadata, the field storing partition brightness data is located. The partition brightness data is a two-dimensional array, with its row and column indices strictly corresponding to the spatial location of the image partitions. The extraction operation reads the value of each element in the array sequentially according to the partition index order (e.g., raster scan order), representing the partition brightness value of each image partition. For the currently read partition brightness value, an arithmetic division operation is performed, with the partition brightness value as the dividend and the global backlight reference value as the divisor. Before the operation, a divisor validity check is introduced to ensure that the global backlight reference value is not zero; if a zero value is encountered, a non-zero minimum default value is used instead. The division operation produces a floating-point result, which is the partition ratio corresponding to the current partition. For example, if the brightness value of a partition is 400 nits and the global backlight reference value is 300 nits, the calculated partition ratio is approximately 1.33. If this ratio is greater than 1, it indicates that the inherent brightness of the partition is higher than the global reference; if it is equal to 1, it indicates that it is consistent with the reference; if it is less than 1, it indicates that it is lower than the reference. This calculation process iterates through all image partitions defined in the metadata and generates a corresponding partition ratio for each partition.

[0048] Each of the partition ratios is compared sequentially with a preset backlight enhancement threshold; If the partition ratio is higher than the backlight enhancement threshold, then an enhanced brightness value is assigned to the corresponding image partition, and first allocation information is generated; If the partition ratio is not higher than the backlight enhancement threshold, then the global backlight reference value is assigned to the corresponding image partition, and second allocation information is generated; According to the preset control rules, all the first allocation information and the second allocation information are structurally integrated to obtain the partition backlight control data.

[0049] Specifically, the final brightness values ​​of all zones are filled into a preset data structure. Based on the physical backlight zone layout of the display (e.g., M rows × N columns), a two-dimensional control matrix with the same dimensions (M × N), or a one-dimensional control array of length M × N, is initialized, where each element's initial value is empty or a default value. All first and second allocation information is traversed. For each piece of information, the zone identifier (e.g., logical row number i and column number j) and the allocated brightness value are extracted. According to a preset mapping relationship (usually a one-to-one correspondence between logical image zones and physical backlight zones), the brightness value is written to the (i, j) position of the control matrix, or its corresponding offset in the one-dimensional array is calculated and written. This positioning-writing process ensures that each physical backlight unit obtains a defined driving brightness value. After all allocation information has been processed, each position in the control matrix or array is filled. The control matrix is ​​serialized or encoded according to a specified data transmission format (e.g., a specific byte sequence, data packet structure) to generate zoned backlight control data.

[0050] The method provided in this embodiment converts the environmental perception results into matching basic backlight parameters by querying a strategy mapping table, providing an accurate benchmark for optimization. Based on the global image brightness in the content-aware metadata, this parameter is corrected to generate an adaptive global backlight benchmark value that integrates environmental and content features, avoiding content brightness distortion. Using this benchmark value as a reference, the brightness ratio of different zones is calculated and compared with a threshold to intelligently allocate enhanced brightness to core high-brightness areas, generating zoned backlight control data. This achieves precise spatial coordination between backlight and image content, improving local contrast and dynamic range while saving energy. The original pixel brightness is normalized to an initial adjustment coefficient and iteratively fused with the dynamic enhancement coefficient to obtain the final pixel brightness adjustment signal, achieving pixel-level contrast enhancement and avoiding over-processing defects.

[0051] In one embodiment, the step of fusing and adjusting the image data of the frame to be displayed based on the partitioned backlight control data and the pixel brightness adjustment signal to obtain image display data includes: The partitioned backlight control data is matched with each image pixel in the frame image data to be displayed to obtain the backlight partition and backlight compensation coefficient of each image pixel; The original luminance component of each image pixel is obtained, and the original luminance component, the corresponding backlight zone, and the backlight compensation coefficient are initially adjusted to obtain the intermediate luminance component. Specifically, adjustments are made by performing two consecutive multiplication operations. The first multiplication operation multiplies the original luminance component by the corresponding adjustment coefficient in the pixel luminance adjustment signal generated for that pixel. This adjustment coefficient carries the intent to optimize contrast and detail based on scene and content collaborative decisions. This multiplication operation produces a content-optimized intermediate luminance value. The second multiplication operation follows immediately, multiplying the content-optimized luminance value obtained above by the backlight compensation coefficient of the current pixel. This operation simulates the impact of changes in the actual backlight intensity received by the pixel on its displayed luminance: if the backlight compensation coefficient is greater than 1, the overall luminance of the point is increased; if it is less than 1, it is decreased. The result of the two multiplication operations is the intermediate luminance component of that pixel.

[0052] Each intermediate brightness component is compared with a preset display brightness threshold. If it exceeds the display brightness threshold, the final brightness component of each image pixel is set to the display brightness threshold. Otherwise, the intermediate brightness component is set to the final brightness component. The final luminance component of each image pixel is converted and combined with the original chrominance component to obtain the image display data.

[0053] Specifically, based on pixel coordinate indices, the final luminance component value at the same location is paired with the original chrominance component value. According to the target color space specification, the paired luminance and chrominance values ​​are combined into a complete pixel description. For example, if the target is YUV format, then (Y... final U original V original The YUV values ​​are converted into (R, G, B) triplets using a predefined color space conversion matrix if RGB format is required. This conversion matrix calculation involves floating-point operations and rounding. The synthesized pixel data is sequentially written to a frame buffer that matches the output resolution. The data in this frame buffer is formatted and encoded according to the electrical interface and protocol requirements of the target display driver chip. This may include packaging the pixel data into a byte stream in a specific order, adding control headers for horizontal and vertical sync signals, and converting it into serial data pairs using low-voltage differential signals. Finally, the image display data is generated.

[0054] The method provided in this embodiment maps environmental awareness to basic backlight parameters and dynamic enhancement coefficients by querying a strategy mapping table, providing an accurate environmental strategy benchmark. Based on content-aware metadata, a global benchmark calculation is performed on the basic values ​​to generate an adaptive backlight benchmark value that integrates environmental and content features, avoiding image brightness distortion. This benchmark value is used for zoned brightness calibration, generating enhanced brightness control data for core high-brightness areas, achieving precise matching between backlight and content spatial distribution, and improving local contrast and depth. Pixel brightness is normalized and iteratively fused with dynamic coefficients to obtain pixel adjustment signals, achieving fine and smooth contrast enhancement and avoiding visual defects.

[0055] Reference Figure 2 As shown, the present invention also provides a display screen dual-sensor image quality optimization device, applied to any of the above-described display screen dual-sensor image quality optimization methods, comprising: The acquisition module is used to acquire the current ambient light parameters of the display screen and the image data of the frame to be displayed; The analysis module is used to analyze and classify the current ambient light parameters to obtain scene mode encoding, and to extract multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata. The association module is used to perform decision calculations on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data. The processing module is used to fuse and adjust the image data of the frame to be displayed according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

[0056] This invention provides a dual-sensor image quality optimization device for displays. By simultaneously acquiring ambient light parameters and image data, and collaboratively calculating and generating pixel-level brightness adjustment and zoned backlight control signals, it achieves deep fusion of scene perception and content perception. It can dynamically optimize the overall screen brightness based on ambient lighting conditions, while simultaneously enhancing local contrast and detail according to the brightness distribution and color characteristics of the image content itself, thus maintaining excellent visibility and image quality reproduction under various ambient lighting conditions. By matching and calibrating the global backlight benchmark with image zone features, it achieves refined on-demand driving of the backlight system, avoiding the energy waste of traditional global dimming in complex content scenarios. Through pixel-level fusion adjustment, it outputs image display data that perfectly matches the perception decisions, ensuring end-to-end collaborative optimization from environmental perception and content analysis to hardware driving, significantly improving the overall energy efficiency of the display while enhancing the visual experience.

[0057] Reference Figure 3 As shown, the present invention also provides a dual-sensor image quality optimization system for a display screen, comprising: Memory, used to store programs; A processor is used to execute the program to implement each step of the display screen dual-sensor image quality optimization method described in any of the above-mentioned embodiments.

[0058] In this embodiment, the processor and memory can be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, digital signal processor, application-specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention.

[0059] The present invention also provides a storage medium storing computer instructions for causing a computer to perform any of the methods described above.

[0060] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0061] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing image quality in a display screen using dual-sensor processing, characterized in that, include: Obtain the current ambient light parameters of the display screen and the image data of the frame to be displayed; The current ambient light parameters are analyzed and classified to obtain scene mode encoding, and multi-dimensional feature extraction is performed on the image data of the frame to be displayed to generate content-aware metadata. Decision-making calculations are performed on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data; The image data of the frame to be displayed is fused and adjusted according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

2. The display screen dual-sensor image quality optimization method according to claim 1, characterized in that, The acquisition of the current ambient light parameters of the display screen and the image data of the frame to be displayed includes: The raw light signal collected by the integrated light sensor of the display screen is acquired, and the raw light signal is converted into the current ambient light parameters; Extract the current frame image data to be displayed from the graphics processing line of the display screen, and compare the current frame image data to be displayed with the previous frame image data to be displayed; If the difference between the current frame image data to be displayed and the previous frame image data to be displayed exceeds a preset display threshold, then the frame image data to be displayed is determined to be valid; otherwise, the frame image data to be displayed from the previous frame is reused.

3. The display screen dual-sensor image quality optimization method according to claim 1, characterized in that, The process involves analyzing and classifying the current ambient light parameters to obtain scene mode encoding, and extracting multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata, including: Based on the light intensity rules of the preset environment mapping table, the current ambient light parameters are used to perform light identification to obtain the light intensity level code; The current ambient light parameters are color temperature identified according to the color temperature rules of the environment mapping table to obtain a color temperature label. The light intensity level code and the color temperature label are then integrated to obtain the scene mode code. Brightness analysis is performed on the image data of the frame to be displayed to obtain brightness distribution characteristic data; Color feature data is obtained by performing color statistics on the image data of the frame to be displayed based on the brightness distribution feature data. The brightness distribution feature data and the color feature data are integrated to obtain the content-aware metadata.

4. The display screen dual-sensor image quality optimization method according to claim 3, characterized in that, The step of performing brightness analysis on the image data to be displayed to obtain brightness distribution feature data includes: The image data of the frame to be displayed is divided into multiple image partitions according to the preset partition grid rules. The pixel brightness of each image partition is calculated in turn to obtain the partition brightness value. Each partition brightness value is compared with a first brightness threshold of the preset partition grid rule. If the partition brightness value is higher than the first brightness threshold, the partition brightness value is marked as a high-brightness partition. Each partition brightness value is compared with a second brightness threshold of the preset partition grid pattern. If the partition brightness value is lower than the second brightness threshold, the partition brightness value is marked as a dark partition. By integrating all the aforementioned partition brightness values, partition brightness data is obtained. The brightness distribution feature data is obtained by integrating the partition brightness data, the bright partition, the dark partition, and the image partition.

5. The display screen dual-sensor image quality optimization method according to claim 1, characterized in that, The decision-making process for the scene mode encoding and the content-aware metadata, respectively, to obtain pixel brightness adjustment signals and zone backlight control data, includes: Based on the scene mode code, the preset scene strategy mapping table is queried to obtain the corresponding basic backlight brightness value and dynamic enhancement coefficient; The scene strategy mapping table predefines a set of image quality optimization strategy parameters that match each scene mode encoding. The dynamic enhancement coefficient is used to control the intensity range of subsequent pixel-level brightness adjustments and the aggressiveness of contrast enhancement; its value range is calibrated to avoid visual artifacts. Based on the content-aware metadata, a global baseline calculation is performed on the basic backlight brightness value to obtain a global backlight baseline value. Based on the global backlight reference value, the content-aware metadata is calibrated for partitioned brightness to obtain the partitioned backlight control data. The pixel brightness of the content-aware metadata is adjusted by coefficients based on the global backlight reference value to obtain the initial brightness adjustment coefficients; The initial brightness adjustment coefficient and the dynamic enhancement coefficient are iteratively fused to obtain the pixel brightness adjustment signal.

6. The display screen dual-sensor image quality optimization method according to claim 5, characterized in that, The step of performing partitioned brightness calibration on the content-aware metadata based on the global backlight reference value to obtain the partitioned backlight control data includes: Extract the partition brightness value of each image partition from the content-aware metadata, and calculate the ratio between each partition brightness value and the global backlight reference value in turn to obtain the corresponding partition ratio. Each of the partition ratios is compared sequentially with a preset backlight enhancement threshold; If the partition ratio is higher than the backlight enhancement threshold, then an enhanced brightness value is assigned to the corresponding image partition, and first allocation information is generated; If the partition ratio is not higher than the backlight enhancement threshold, then the global backlight reference value is assigned to the corresponding image partition, and second allocation information is generated; According to the preset control rules, all the first allocation information and the second allocation information are structurally integrated to obtain the partition backlight control data.

7. The display screen dual-sensor image quality optimization method according to claim 1, characterized in that, The step of fusing and adjusting the image data of the frame to be displayed based on the partition backlight control data and the pixel brightness adjustment signal to obtain image display data includes: The partitioned backlight control data is matched with each image pixel in the frame image data to be displayed to obtain the backlight partition and backlight compensation coefficient of each image pixel; The original luminance component of each image pixel is obtained, and the original luminance component, the corresponding backlight zone, and the backlight compensation coefficient are initially adjusted to obtain the intermediate luminance component. Each intermediate brightness component is compared with a preset display brightness threshold. If it exceeds the display brightness threshold, the final brightness component of each image pixel is set to the display brightness threshold. Otherwise, the intermediate brightness component is set to the final brightness component. The final luminance component of each image pixel is converted and combined with the original chrominance component to obtain the image display data.

8. A dual-sensor image quality optimization device for a display screen, characterized in that, The display screen dual-sensor image quality optimization method applied to any one of claims 1-7 includes: The acquisition module is used to acquire the current ambient light parameters of the display screen and the image data of the frame to be displayed; The analysis module is used to analyze and classify the current ambient light parameters to obtain scene mode encoding, and to extract multi-dimensional features from the image data of the frame to be displayed to generate content-aware metadata. The association module is used to perform decision calculations on the scene mode encoding and the content-aware metadata respectively to obtain pixel brightness adjustment signals and partition backlight control data. The processing module is used to fuse and adjust the image data of the frame to be displayed according to the partition backlight control data and the pixel brightness adjustment signal to obtain image display data.

9. A dual-sensor image quality optimization system for a display screen, characterized in that, include: Memory, used to store programs; A processor is configured to execute the program to implement the various steps of the display screen dual-sensor image quality optimization method as described in any one of claims 1-7.

10. A storage medium, characterized in that, The computer contains computer instructions for causing the computer to perform the method according to any one of claims 1 to 7.

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