A picture quality adjusting method, device, equipment and medium

Abstract

Embodiments of the present disclosure relate to a picture quality adjustment method, device, equipment and medium, wherein the method comprises: acquiring a multimedia resource, the multimedia resource comprising a video or an image; determining a scene detection result and a picture quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate a semantic result of at least one dimension of the multimedia resource, and the picture quality detection result is used to indicate an image quality of the multimedia resource; determining a picture quality enhancement strategy based on the scene detection result and the picture quality detection result, and performing picture quality enhancement processing on the multimedia resource according to the picture quality enhancement strategy, wherein the picture quality enhancement strategy comprises at least one picture quality enhancement algorithm. Embodiments of the present disclosure achieve adaptive and targeted picture quality enhancement, significantly improve the effect of picture quality enhancement, and further greatly improve the experience effect of users.

Description

Technical Field

[0001] This disclosure relates to the field of image processing technology, and more particularly to an interactive tool generation device, apparatus, equipment, and medium. Background Technology

[0002] With the continuous development of internet technology and electronic devices, users have increasingly higher requirements for image or video quality.

[0003] To improve user experience, image quality can be enhanced using image enhancement algorithms. However, current image enhancement methods are relatively simple and their effects are insufficient. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this disclosure provides an image quality adjustment method, apparatus, device and medium.

[0005] This disclosure provides an image quality adjustment method, the method comprising:

[0006] Acquire multimedia resources, including videos or images;

[0007] Determine the scene detection result and image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource;

[0008] Based on the scene detection results and the image quality detection results, an image quality enhancement strategy is determined, and the multimedia resources are processed to enhance image quality according to the image quality enhancement strategy. The image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0009] This disclosure also provides an image quality adjustment device, the device comprising:

[0010] The resource acquisition module is used to acquire multimedia resources, including videos or images;

[0011] A scene quality module is used to determine the scene detection result and the image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource;

[0012] The image quality enhancement module is used to determine an image quality enhancement strategy based on the scene detection results and the image quality detection results, and to perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0013] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the image quality adjustment method provided in this disclosure.

[0014] This disclosure also provides a computer-readable storage medium storing a computer program for performing the image quality adjustment method provided in this disclosure.

[0015] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: The image quality adjustment scheme provided in this disclosure acquires multimedia resources, including videos or images; determines the scene detection results and image quality detection results corresponding to the multimedia resources; determines an image quality enhancement strategy based on the scene detection results and image quality detection results; and performs image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy. The image quality enhancement strategy includes at least one image quality enhancement algorithm. Using the above technical solution, a corresponding image quality enhancement strategy can be determined based on the scene and image quality of the video or image, and the image quality effect can be enhanced using this strategy. Since the image quality enhancement strategy is determined based on information from both the scene and image quality dimensions, and can be composed of one or more image quality enhancement algorithms, adaptive and targeted image quality enhancement is achieved, significantly improving the image quality enhancement effect and thus greatly enhancing the user experience. Attached Figure Description

[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0017] Figure 1 A schematic flowchart illustrating an image quality adjustment method provided in this embodiment of the disclosure;

[0018] Figure 2 A flowchart illustrating another image quality adjustment method provided in this embodiment of the disclosure;

[0019] Figure 3 A schematic diagram illustrating an image quality adjustment process provided in an embodiment of this disclosure;

[0020] Figure 4 A schematic diagram of an algorithmic routing table provided in an embodiment of this disclosure;

[0021] Figure 5 This is a schematic diagram of the structure of an image quality adjustment device provided in an embodiment of the present disclosure;

[0022] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0025] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0026] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0027] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0028] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0029] Image enhancement is a feature in image or video editing tools, typically offering adjustments across many finer dimensions, such as saturation, contrast, sharpness, highlights, and shadows. However, understanding the meaning of these dimensions and making appropriate adjustments requires a certain level of expertise, making it unfriendly to the average user. Furthermore, complex parameter adjustments significantly increase the editing workload, reducing the efficiency of video or image publishing and ultimately impacting the user experience.

[0030] To reduce the workload of user parameter tuning while enhancing image quality, some automated image enhancement algorithms have emerged, such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Unsharp Mask (USM) algorithms. However, current image enhancement algorithms are relatively simple, typically performing simple automatic enhancements on a single dimension, such as contrast enhancement, sharpening, and noise reduction; or using a fixed set of automatic enhancement algorithms. In real-world scenarios, however, various image quality problems may exist, and the aforementioned methods, being relatively simple, cannot achieve satisfactory results, failing to meet the required image enhancement effects. To address these issues, this disclosure provides an image quality adjustment method, which will be described below with reference to specific embodiments.

[0031] Figure 1 This is a flowchart illustrating an image quality adjustment method provided in an embodiment of this disclosure. The method can be executed by an image quality adjustment device, which can be implemented using software and / or hardware, and is generally integrated into an electronic device. Figure 1 As shown, the method includes:

[0032] Step 101: Obtain multimedia resources, including videos or images.

[0033] The multimedia resources can be any video or image that requires image quality enhancement, with no restrictions on the specific file format and source. For example, multimedia resources can be videos or images captured in real time, or videos or images downloaded from the Internet.

[0034] Step 102: Determine the scene detection results and image quality detection results corresponding to the multimedia resources.

[0035] The scene detection result is used to indicate at least one dimension of the semantic result of the multimedia resource. A scene is a type of semantics, and the scene semantics expressed by the multimedia resource can include the described object and scene category, etc. The scene detection result can be understood as the result obtained by detecting the scene semantics of the multimedia resource in one or more dimensions. In this embodiment of the disclosure, the scene detection result may include at least one of day / night results, target object detection results, exposure level, etc., and the target object can be a face.

[0036] Image quality detection results are used to indicate the image quality of multimedia resources. Image quality detection results refer to the results of parameter detection of multimedia resources related to display effects. In this embodiment of the disclosure, image quality detection results may include noise level and / or blur level, etc. Noise refers to unnecessary or redundant interference information existing in an image or video.

[0037] In this embodiment of the disclosure, after acquiring multimedia resources, multi-dimensional detection algorithms can be invoked to perform scene detection and image quality detection on the multimedia resources, thereby determining the corresponding scene detection results and image quality detection results. The determination of noise level and blur level in the image quality detection results can be achieved in various ways, and this embodiment of the disclosure does not limit this method. For example, image quality detection of multimedia resources is performed using a neural network-based noise recognition model; the blur level of multimedia resources is identified by determining the peak signal-to-noise ratio (PSNR). The PSNR is inversely proportional to the blur level; that is, the higher the PSNR, the lower the blur level of the multimedia resources.

[0038] Optionally, determining the scene detection result corresponding to the multimedia resource may include: using a deep learning model for day and night classification to detect the multimedia resource and determine the day and night result corresponding to the multimedia resource, wherein the day and night result includes daytime and nighttime; and / or, determining the face detection result of the multimedia resource through a face recognition algorithm.

[0039] The deep learning model for day / night classification can be any of several neural network-based classification models, such as a Support Vector Machine (SVM) classifier or a Convolutional Neural Network (CNN) for day / night classification, depending on the specific circumstances. Specifically, for multimedia resources, their brightness histograms can be statistically analyzed and classified using an SVM classifier, or the resolution of the multimedia resources can be adjusted before classification using a CNN, resulting in a detection result of day or night.

[0040] A face recognition algorithm can be any algorithm capable of performing face recognition; for example, it can be a convolutional neural network for face recognition. Specifically, a convolutional neural network for face recognition can be used to extract face regions from multimedia resources. Alternatively, face regions from multimedia resources can be extracted and matched using preset face feature points. The resulting face detection result may or may not include face regions.

[0041] Optionally, the exposure level in the scene detection results corresponding to the multimedia resources can be determined using an Automatic Exposure Control (AEC) system. The exposure level in this embodiment can include underexposure, normal exposure, and overexposure.

[0042] Optionally, in this embodiment of the present disclosure, the face detection results and exposure levels in the scene can be corrected for day and night results. For example, if the multimedia resource is daytime, but the shooting is done in an indoor place with insufficient light, the day and night results may be misjudged as nighttime. In this case, if the face detection result shows that there is a face area and the exposure level is normal exposure or overexposure, it can be said that the day and night results are daytime.

[0043] Step 103: Determine the image quality enhancement strategy based on the scene detection results and image quality detection results, and perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0044] The image quality enhancement strategy can be a comprehensive solution (pipeline) for enhancing the image quality of multimedia resources. This strategy may include at least one image quality enhancement algorithm, which can automatically detect multimedia resources and selectively process the areas requiring enhancement; deep learning algorithms are typically employed. When the image quality enhancement strategy includes multiple algorithms, these algorithms have an execution order, which can be determined based on the specific circumstances.

[0045] In this embodiment, the image quality enhancement algorithm may include at least one of the following: a noise reduction algorithm, a color brightness enhancement algorithm, a skin tone protection algorithm, and a sharpening algorithm. The color brightness enhancement algorithm can be implemented based on a deep neural network. This deep neural network-based color brightness enhancement algorithm can be achieved by training a convolutional neural network using a color brightness enhancement dataset, and then using the trained convolutional neural network to enhance the color brightness of multimedia resources. The skin tone protection algorithm involves extracting the skin tone range from the face region in the multimedia resource, then performing skin tone detection and segmentation within the face region, and finally feathering and blurring the mask of the skin tone region.

[0046] Optionally, determining the image quality enhancement strategy based on the scene detection results and image quality detection results may include: determining the corresponding image quality enhancement strategy by looking up the algorithm routing table or using an algorithm branch decision tree based on the scene detection results and image quality detection results. Here, the algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch decision strategies.

[0047] The algorithm routing table can be a routing table that includes image enhancement strategies under multiple different conditions, and each image enhancement strategy is composed of at least one image enhancement algorithm. The algorithm branch decision tree can be a decision tree that includes multiple branch decision strategies, and the decision strategies of each branch have a sequential execution order.

[0048] Specifically, after determining the scene detection results and image quality detection results corresponding to the multimedia resources, the corresponding image quality enhancement strategy, composed of at least one image quality enhancement algorithm, can be determined by searching the algorithm routing table based on these results. Alternatively, the scene detection results and image quality detection results can be input into the algorithm branch decision tree, and branch judgments can be performed one by one according to the preset execution order of multiple branch judgment strategies. After each branch judgment strategy, the image quality enhancement algorithm corresponding to the current branch judgment result can be determined. Finally, after the judgment is completed, an image quality enhancement strategy composed of at least one image quality enhancement algorithm can be obtained. Then, the image quality enhancement strategy can be used to enhance the image quality of the multimedia resources, resulting in enhanced multimedia resources.

[0049] Optionally, when determining the image quality enhancement algorithm for multimedia resources, it can also be based on the meta-information of the multimedia resources. The meta-information can include the attribute information of the multimedia resource; for example, if the multimedia resource is a video, the meta-information can be the video title or video summary. Keywords can be extracted from the meta-information of the multimedia resource, and the corresponding image quality enhancement algorithm can be determined based on the mapping relationship between the keywords and a pre-established mapping relationship between keywords and image quality enhancement algorithms.

[0050] Optionally, after determining the image enhancement algorithm that includes at least one image enhancement algorithm, it can be described using an execution graph composed of algorithm nodes, and the algorithm nodes can be subjected to chain-like serial processing, branch-like parallel processing, or a combination of the above two processing methods, which is not limited.

[0051] The image quality adjustment scheme provided in this disclosure acquires multimedia resources, including videos or images; determines the scene detection results and image quality detection results corresponding to the multimedia resources; determines an image quality enhancement strategy based on the scene detection results and image quality detection results; and performs image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy. The image quality enhancement strategy includes at least one image quality enhancement algorithm. Using the above technical solution, a corresponding image quality enhancement strategy can be determined based on the scene and image quality of the video or image, and the image quality effect can be enhanced using this strategy. Since the image quality enhancement strategy is determined based on information from both scene and image quality dimensions, and can be composed of one or more image quality enhancement algorithms, adaptive and targeted image quality enhancement is achieved, significantly improving the image quality enhancement effect and thus greatly enhancing the user experience.

[0052] In some embodiments, when the multimedia resource is a video, determining the scene detection result and image quality detection result corresponding to the multimedia resource may include: extracting multiple key frames from the multimedia resource; and determining the scene detection result and image quality detection result corresponding to the multimedia resource by detecting the multiple key frames.

[0053] A keyframe can be one of multiple video frames included in a video. A keyframe represents a segment of video, while a video frame is the smallest unit that constitutes a video. When the multimedia resource is a video, multiple keyframes can be extracted from the video. By performing scene detection and image quality detection on each of these keyframes, scene detection and image quality detection of the multimedia resource can be achieved, yielding the scene detection results and image quality detection results.

[0054] Optionally, extracting multiple keyframes from multimedia resources may include: dividing the multimedia resources into multiple video segments, where the similarity between two adjacent video segments is less than a preset threshold; and extracting multiple keyframes from each video segment. Keyframes can be used to represent a video segment, and they can be obtained by uniformly extracting keyframes from the video segments; the specific number can be determined based on the actual situation.

[0055] Specifically, when the multimedia resource is video, the video can first be divided into multiple video segments with continuous scenes through transition detection. The transition detection process involves determining the similarity between two adjacent frames of the video. If the similarity is less than a preset threshold, it indicates that the scene between the two adjacent frames has changed. The video can be divided using the middle of the two adjacent frames as the dividing line. The two resulting video segments each include the two adjacent frames, so the similarity between the two video segments is also less than the preset threshold. The preset threshold can be determined based on the actual situation. After dividing the video into multiple video segments, multiple keyframes can be extracted from each video segment.

[0056] Optionally, determining the scene detection results and image quality detection results corresponding to the multimedia resources by detecting multiple keyframes may include: determining the segment scene detection results and segment image quality detection results corresponding to each video segment by detecting the scene and image quality of multiple keyframes included in each video segment.

[0057] After extracting multiple keyframes from each video segment as described above, the keyframes can be used as input for subsequent scene and image quality detection. When determining the scene detection results and image quality detection results of the multimedia resources, processing can be performed on a video segment-by-segment basis. That is, by performing scene detection and image quality detection on multiple keyframes included in each video segment, the scene detection results and image quality detection results corresponding to each video segment can be determined. The specific determination method is as described in the above embodiment and will not be repeated here.

[0058] Since each video segment contains multiple keyframes, the scene detection results and image quality detection results corresponding to multiple keyframes can be aggregated to determine the scene and image quality of each video segment. Taking the detection results of one dimension of the scene detection results and image quality detection results of a video segment as an example, the specific information aggregation process can include: counting the number of detection results of the target dimension for multiple keyframes, determining the number of keyframes corresponding to each detection result, and determining the detection results with a number of keyframes greater than or equal to a preset number as the final detection result for the target dimension. The preset number can be greater than or equal to half the number of keyframes. If the number of keyframes corresponding to each detection result is the same, then the confidence level of each detection result is determined, and the detection result with the highest confidence level is determined as the final detection result for the target dimension. The above aggregation of results can first determine the final detection result by voting based on the classification results. If it cannot be determined, the final detection result can be determined by further judgment based on the numerical results.

[0059] In some embodiments, performing image quality enhancement processing on multimedia resources includes: performing image quality enhancement processing on each video segment in the multimedia resources according to a segment image quality enhancement algorithm determined based on the segment scene detection result and the segment image quality detection result corresponding to each video segment.

[0060] For the multimedia resources divided into multiple video segments, after determining the scene detection results and image quality detection results for each video segment, image quality enhancement processing can be performed on a segment-by-segment basis. That is, based on the scene detection results and image quality detection results corresponding to each video segment, the corresponding segment image quality enhancement algorithm is determined by looking up the algorithm routing table or by using the algorithm branch decision tree. Then, the segment image quality enhancement algorithm is used to perform image quality enhancement processing on each video segment to obtain the enhanced video segments.

[0061] The above solution not only enhances the image quality of the video, but also applies corresponding image quality enhancement methods to video segments in different scenes, making the image quality enhancement effect more accurate and targeted, and thus making the image quality effect of the enhanced video more diverse.

[0062] Figure 2 This is a flowchart illustrating another image quality adjustment method provided in this embodiment. This embodiment further optimizes the above-described image quality adjustment method based on the previous embodiment. Figure 2 As shown, the method includes:

[0063] Step 201: Obtain multimedia resources.

[0064] Multimedia resources include videos or images.

[0065] Step 202: Determine the scene detection results and image quality detection results corresponding to the multimedia resources.

[0066] The scene detection results include at least one of the following: day / night results, target object detection results, and exposure level; the image quality detection results include noise level and / or blur level.

[0067] Optionally, determining the scene detection result corresponding to the multimedia resource includes: using a deep learning model for day and night classification to detect the multimedia resource and determining the day and night result corresponding to the multimedia resource, wherein the day and night result includes daytime and nighttime; and / or, determining the face detection result of the multimedia resource through a face recognition algorithm.

[0068] Step 203: Based on the scene detection results and image quality detection results, determine the corresponding image quality enhancement strategy by searching the algorithm routing table or using the algorithm branch decision tree.

[0069] The image quality enhancement strategy includes at least one image quality enhancement algorithm. Optionally, the image quality enhancement algorithm includes at least one of a noise reduction algorithm, a color and brightness enhancement algorithm, a skin tone protection algorithm, and a sharpening algorithm. The algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch decision strategies.

[0070] Optionally, the algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies.

[0071] Optionally, when the multimedia resource is a video, determining the scene detection result and image quality detection result corresponding to the multimedia resource includes: extracting multiple key frames from the multimedia resource; and determining the scene detection result and image quality detection result corresponding to the multimedia resource by detecting the multiple key frames.

[0072] Optionally, extracting multiple keyframes from the multimedia resource may include: dividing the multimedia resource into multiple video segments, where the similarity between two adjacent video segments is less than a preset threshold; and extracting multiple keyframes from each video segment. Optionally, determining the scene detection result and image quality detection result corresponding to the multimedia resource by detecting multiple keyframes includes: determining the segment scene detection result and segment image quality detection result corresponding to each video segment by performing scene detection and image quality detection on the multiple keyframes included in each video segment.

[0073] Step 204: Perform image enhancement processing on multimedia resources according to the image enhancement strategy.

[0074] Optionally, when the multimedia resource is a video, the multimedia resource is subjected to image quality enhancement processing, including: performing image quality enhancement processing on each video segment in the multimedia resource according to the segment scene detection results and segment image quality detection results determined by the segment image quality enhancement algorithm corresponding to each video segment.

[0075] For example, Figure 3 This is a schematic diagram illustrating an image quality adjustment process provided in an embodiment of this disclosure. Figure 3 Using video as an example of multimedia resources, this invention illustrates the image quality adjustment process provided in embodiments of the present disclosure. Figure 3 As shown, the specific process may include: 1. First, the video is divided into segments of continuous scenes through transition detection, as shown in the figure where the complete video is divided into multiple video segments. 2. For each video segment, several frames are extracted as input for scene and image quality detection. 3. Detection algorithms are called to perform scene and image quality detection on the extracted frames respectively. Detection dimensions include, but are not limited to, day / night detection, noise detection, exposure detection, face detection, and blur detection shown in the figure. Among them, day / night results, exposure level, and face detection belong to scene detection results, while noise level and blur level belong to image quality detection results. 4. The scene and image quality detection results of multiple frames are aggregated to obtain the scene and image quality of each video segment. 5. Based on the scene, image quality, and meta-description information carried by each video segment, an image quality enhancement scheme (pipeline) corresponding to the video can be generated. The image quality enhancement scheme can be composed of multiple processing algorithms with a sequential execution order. 6. Specific methods may include: a) sequential algorithmic paths using scene, image quality, and descriptive information as conditions; b) algorithmic branching decision trees using scene, image quality, and descriptive information as conditions. 7. Image quality enhancement schemes can be described using a graph composed of algorithm nodes. Each algorithm allows for chain-like serial processing, branch-like parallel processing, and combinations of the two processing schemes mentioned above. For example... Figure 3 The image enhancement scheme determined in the code can include four algorithms: noise reduction, color and brightness enhancement, skin tone protection, and sharpening. The arrows indicate the execution order. Color and brightness enhancement and skin tone protection can be processed in parallel. 8. Perform image enhancement processing on each video segment according to the image enhancement scheme corresponding to each video segment to obtain the enhanced video segment.

[0076] For example, Figure 4 This is a schematic diagram of an algorithmic routing table provided in an embodiment of this disclosure, as shown below. Figure 4As shown, an exemplary algorithm routing table is illustrated. This table can be pre-built and stored. During actual use, after determining the scene and image quality, the corresponding image enhancement strategy can be determined by looking up the algorithm routing table. For example, the scene and image quality in the first column of the figure are: night scene (i.e., nighttime), underexposure, noise level range [a, b], face detected, and no blur. The corresponding image enhancement strategies can include the four image enhancement algorithms shown in the figure: noise reduction, color brightness enhancement, skin tone protection, and sharpening. The execution order is as follows: Figure 4 As shown in the figure, circles with different attributes can be used to represent different things using different algorithms. For example, circles with different gray levels or different fill colors can be used.

[0077] In the above scheme, when the multimedia resource is video, the video is divided into continuous segments, and scene detection and image quality detection are performed on each continuous scene segment to obtain its scene and image quality information. Then, based on the scene and image quality information, an image quality enhancement scheme composed of multiple algorithms is generated based on routing tables or decision trees, and each video segment is enhanced.

[0078] This solution proposes a scene- and image quality-based enhancement scheme for videos or images with unknown scenes and image quality. By detecting and analyzing existing image quality problems in the video or image, it automatically selects an appropriate comprehensive solution and algorithm parameters. By automatically combining multiple different algorithms, it achieves adaptive and targeted image quality enhancement, avoiding the problem of low accuracy caused by a single enhancement algorithm or fixed algorithm process being unable to adapt to all scenes. This significantly improves the effect of image quality enhancement and greatly reduces the workload of users' editing.

[0079] The image quality adjustment scheme provided in this disclosure involves acquiring multimedia resources, including videos or images; determining the scene detection results and image quality detection results corresponding to the multimedia resources; determining the corresponding image quality enhancement strategy based on the scene detection results and image quality detection results by searching an algorithm routing table or using an algorithm branch decision tree; and performing image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy. By adopting the above technical solution, a corresponding image quality enhancement strategy can be determined based on the scene and image quality of the video or image, and this strategy can be used to enhance the image quality effect. Since the image quality enhancement strategy is determined based on information from both the scene and image quality dimensions, and can be composed of one or more image quality enhancement algorithms, adaptive and targeted image quality enhancement is achieved, significantly improving the image quality enhancement effect and thus greatly enhancing the user experience.

[0080] Figure 5 This is a schematic diagram of the structure of an image quality adjustment device provided in an embodiment of this disclosure. The device can be implemented by software and / or hardware, and is generally integrated into an electronic device. Figure 5As shown, the device includes:

[0081] Resource acquisition module 301 is used to acquire multimedia resources, including videos or images;

[0082] Scene image quality module 302 is used to determine the scene detection result and image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource;

[0083] The image quality enhancement module 303 is used to determine an image quality enhancement strategy based on the scene detection results and the image quality detection results, and to perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0084] Optionally, the scene detection results include at least one of day / night results, target object detection results, and exposure level, and the image quality detection results include noise level and / or blur level.

[0085] Optionally, the scene image quality module 302 is specifically used for:

[0086] Based on the scene detection results and the image quality detection results, the corresponding image quality enhancement strategy is determined by searching the algorithm routing table or by using the algorithm branch decision tree.

[0087] Optionally, the algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies.

[0088] Optionally, when the image quality enhancement strategy includes multiple image quality enhancement algorithms, the multiple image quality enhancement algorithms have an execution order.

[0089] Optionally, when the multimedia resource is video, the scene image quality module 302 includes:

[0090] A frame extraction unit is used to extract multiple keyframes from the multimedia resource.

[0091] The detection unit is used to determine the scene detection result and image quality detection result corresponding to the multimedia resource by detecting the multiple key frames.

[0092] Optionally, the frame extraction unit is specifically used for:

[0093] The multimedia resources are divided into multiple video segments, and the similarity between two adjacent video segments is less than a preset threshold.

[0094] For each video segment, extract multiple keyframes.

[0095] Optionally, the detection unit is used for:

[0096] By performing scene detection and image quality detection on the multiple keyframes included in each video segment, the scene detection result and image quality detection result corresponding to each video segment are determined.

[0097] Optionally, the image quality enhancement module 303 is specifically used for:

[0098] Based on the segment scene detection results and segment image quality detection results corresponding to each video segment, the segment image quality enhancement algorithm is determined, and image quality enhancement processing is performed on each video segment in the multimedia resource respectively.

[0099] Optionally, the image quality enhancement algorithm includes at least one of a noise reduction algorithm, a color brightness enhancement algorithm, a skin color protection algorithm, and a sharpening algorithm.

[0100] The image quality adjustment device provided in this disclosure can execute the image quality adjustment method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method.

[0101] This disclosure also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the image quality adjustment method provided in any embodiment of this disclosure.

[0102] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present disclosure. See below for details. Figure 6 The diagram illustrates a structural schematic suitable for implementing the electronic device 400 in the embodiments of this disclosure. The electronic device 400 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0103] like Figure 6As shown, electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage device 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of electronic device 400. Processing device 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0104] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 400 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0105] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from storage device 408, or installed from ROM 402. When the computer program is executed by processing device 401, it performs the functions defined in the image quality adjustment method of embodiments of this disclosure.

[0106] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0107] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0108] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0109] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire multimedia resources, the multimedia resources including videos or images; determine scene detection results and image quality detection results corresponding to the multimedia resources, wherein the scene detection results are used to indicate at least one dimension of the semantic results of the multimedia resources, and the image quality detection results are used to indicate the image quality of the multimedia resources; determine an image quality enhancement strategy based on the scene detection results and the image quality detection results, and perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0110] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] 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 this 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 the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can 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.

[0112] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0113] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0114] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0115] According to one or more embodiments of this disclosure, this disclosure provides an image quality adjustment method, including:

[0116] Acquire multimedia resources, including videos or images;

[0117] Determine the scene detection result and image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource;

[0118] Based on the scene detection results and the image quality detection results, an image quality enhancement strategy is determined, and the multimedia resources are processed to enhance image quality according to the image quality enhancement strategy. The image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0119] According to one or more embodiments of this disclosure, in the image quality adjustment method provided by this disclosure, the scene detection result includes at least one of day / night result, target object detection result, and exposure level, and the image quality detection result includes noise level and / or blur level.

[0120] According to one or more embodiments of this disclosure, the image quality adjustment method provided by this disclosure, which determines an image quality enhancement strategy based on the scene detection result and the image quality detection result, includes:

[0121] Based on the scene detection results and the image quality detection results, the corresponding image quality enhancement strategy is determined by searching the algorithm routing table or by using the algorithm branch decision tree.

[0122] According to one or more embodiments of this disclosure, in the image quality adjustment method provided by this disclosure, the algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies.

[0123] According to one or more embodiments of this disclosure, in the image quality adjustment method provided by this disclosure, when the image quality enhancement strategy includes multiple image quality enhancement algorithms, the multiple image quality enhancement algorithms have an execution order.

[0124] According to one or more embodiments of this disclosure, in the image quality adjustment method provided by this disclosure, when the multimedia resource is a video, determining the scene detection result and image quality detection result corresponding to the multimedia resource includes:

[0125] Extract multiple keyframes from the multimedia resource;

[0126] The scene detection results and image quality detection results corresponding to the multimedia resources are determined by detecting the multiple key frames.

[0127] According to one or more embodiments of this disclosure, the image quality adjustment method provided by this disclosure includes extracting multiple keyframes from the multimedia resource, including:

[0128] The multimedia resources are divided into multiple video segments, and the similarity between two adjacent video segments is less than a preset threshold.

[0129] For each video segment, extract multiple keyframes.

[0130] According to one or more embodiments of this disclosure, the image quality adjustment method provided by this disclosure determines the scene detection result and image quality detection result corresponding to the multimedia resource by detecting the plurality of key frames, including:

[0131] By performing scene detection and image quality detection on the multiple keyframes included in each video segment, the scene detection result and image quality detection result corresponding to each video segment are determined.

[0132] According to one or more embodiments of this disclosure, the image quality adjustment method provided in this disclosure performs image quality enhancement processing on the multimedia resources, including:

[0133] Based on the segment scene detection results and segment image quality detection results corresponding to each video segment, the segment image quality enhancement algorithm is determined, and image quality enhancement processing is performed on each video segment in the multimedia resource respectively.

[0134] According to one or more embodiments of this disclosure, the image quality adjustment method provided by this disclosure includes at least one of a noise reduction algorithm, a color brightness enhancement algorithm, a skin tone protection algorithm, and a sharpening algorithm.

[0135] According to one or more embodiments of this disclosure, this disclosure provides an image quality adjustment device, comprising:

[0136] The resource acquisition module is used to acquire multimedia resources, including videos or images;

[0137] A scene quality module is used to determine the scene detection result and the image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource;

[0138] The image quality enhancement module is used to determine an image quality enhancement strategy based on the scene detection results and the image quality detection results, and to perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm.

[0139] According to one or more embodiments of the present disclosure, in the image quality adjustment device provided by the present disclosure, the scene detection result includes at least one of day / night result, target object detection result, and exposure level, and the image quality detection result includes noise level and / or blur level.

[0140] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, the scene image quality module is specifically used for:

[0141] Based on the scene detection results and the image quality detection results, the corresponding image quality enhancement strategy is determined by searching the algorithm routing table or by using the algorithm branch decision tree.

[0142] According to one or more embodiments of the present disclosure, in the image quality adjustment device provided by the present disclosure, the algorithm routing table is a routing table that includes multiple image quality enhancement strategies, and the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies.

[0143] According to one or more embodiments of the present disclosure, in the image quality adjustment device provided by the present disclosure, when the image quality enhancement strategy includes multiple image quality enhancement algorithms, the multiple image quality enhancement algorithms have an execution order.

[0144] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, when the multimedia resource is video, the scene image quality module includes:

[0145] A frame extraction unit is used to extract multiple keyframes from the multimedia resource.

[0146] The detection unit is used to determine the scene detection result and image quality detection result corresponding to the multimedia resource by detecting the multiple key frames.

[0147] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, the frame extraction unit is specifically used for:

[0148] The multimedia resources are divided into multiple video segments, and the similarity between two adjacent video segments is less than a preset threshold.

[0149] For each video segment, extract multiple keyframes.

[0150] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, the detection unit is used for:

[0151] By performing scene detection and image quality detection on the multiple keyframes included in each video segment, the scene detection result and image quality detection result corresponding to each video segment are determined.

[0152] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, the image quality enhancement module is specifically used for:

[0153] Based on the segment scene detection results and segment image quality detection results corresponding to each video segment, the segment image quality enhancement algorithm is determined, and image quality enhancement processing is performed on each video segment in the multimedia resource respectively.

[0154] According to one or more embodiments of this disclosure, in the image quality adjustment device provided by this disclosure, the image quality enhancement algorithm includes at least one of a noise reduction algorithm, a color brightness enhancement algorithm, a skin tone protection algorithm, and a sharpening algorithm.

[0155] According to one or more embodiments of this disclosure, this disclosure provides an electronic device, including:

[0156] processor;

[0157] Memory used to store the processor's executable instructions;

[0158] The processor is configured to read the executable instructions from the memory and execute the instructions to implement any of the image quality adjustment methods provided in this disclosure.

[0159] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium storing a computer program for performing any of the image quality adjustment methods provided in the present disclosure.

[0160] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0161] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0162] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for adjusting image quality, characterized in that, include: Acquire multimedia resources, including videos or images; Determine the scene detection result and image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource; Based on the scene detection results and the image quality detection results, an image quality enhancement strategy is determined, and the multimedia resources are processed to enhance image quality according to the image quality enhancement strategy. The image quality enhancement strategy includes at least one image quality enhancement algorithm. When the image quality enhancement strategy includes multiple image quality enhancement algorithms, the multiple image quality enhancement algorithms have an execution order; When the multimedia resource is a video, the scene detection result and image quality detection result corresponding to the multimedia resource are determined, including: Extract multiple keyframes from the multimedia resource; The scene detection results and image quality detection results corresponding to the multimedia resources are determined by detecting the multiple key frames. The extraction of multiple keyframes from the multimedia resources includes: The multimedia resources are divided into multiple video segments, and the similarity between two adjacent video segments is less than a preset threshold. For each video segment, extract multiple keyframes; The process of determining an image quality enhancement strategy based on the scene detection results and the image quality detection results includes: Based on the scene detection results and the image quality detection results, a corresponding image quality enhancement strategy is determined by using an algorithm branch decision tree, wherein the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies; The step of determining the corresponding image enhancement strategy by employing an algorithmic branch decision tree includes: The scene detection results and the image quality detection results are input into the algorithm branch decision tree. The branch judgments are performed one by one according to the preset execution order of the multiple branch judgment strategies. After each branch judgment strategy, the image quality enhancement algorithm corresponding to the current branch judgment result is determined. After the judgment is completed, an image quality enhancement strategy composed of at least one of the image quality enhancement algorithms is obtained.

2. The method according to claim 1, characterized in that, The scene detection results include at least one of day / night results, target object detection results, and exposure level, and the image quality detection results include noise level and / or blur level.

3. The method according to claim 1, characterized in that, The scene detection results and image quality detection results corresponding to the multimedia resources are determined by detecting the multiple keyframes, including: By performing scene detection and image quality detection on the multiple keyframes included in each video segment, the scene detection result and image quality detection result corresponding to each video segment are determined.

4. The method according to claim 1, characterized in that, The multimedia resources are subjected to image quality enhancement processing, including: Based on the segment scene detection results and segment image quality detection results corresponding to each video segment, the segment image quality enhancement algorithm is determined, and image quality enhancement processing is performed on each video segment in the multimedia resource respectively.

5. The method according to claim 1, characterized in that, The image quality enhancement algorithm includes at least one of the following: noise reduction algorithm, color brightness enhancement algorithm, skin color protection algorithm, and sharpening algorithm.

6. An image quality adjustment device, characterized in that, include: The resource acquisition module is used to acquire multimedia resources, including videos or images; A scene quality module is used to determine the scene detection result and the image quality detection result corresponding to the multimedia resource, wherein the scene detection result is used to indicate the semantic result of at least one dimension of the multimedia resource, and the image quality detection result is used to indicate the image quality of the multimedia resource; The image quality enhancement module is used to determine an image quality enhancement strategy based on the scene detection results and the image quality detection results, and to perform image quality enhancement processing on the multimedia resources according to the image quality enhancement strategy, wherein the image quality enhancement strategy includes at least one image quality enhancement algorithm; When the image quality enhancement strategy includes multiple image quality enhancement algorithms, the multiple image quality enhancement algorithms have an execution order; When the multimedia resource is a video, the scene detection result and image quality detection result corresponding to the multimedia resource are determined, including: Extract multiple keyframes from the multimedia resource; The scene detection results and image quality detection results corresponding to the multimedia resources are determined by detecting the multiple key frames. The extraction of multiple keyframes from the multimedia resources includes: The multimedia resources are divided into multiple video segments, and the similarity between two adjacent video segments is less than a preset threshold. For each video segment, extract multiple keyframes; The process of determining an image quality enhancement strategy based on the scene detection results and the image quality detection results includes: Based on the scene detection results and the image quality detection results, a corresponding image quality enhancement strategy is determined by using an algorithm branch decision tree, wherein the algorithm branch decision tree is a decision tree that includes multiple branch judgment strategies; The step of determining the corresponding image enhancement strategy by employing an algorithmic branch decision tree includes: The scene detection results and the image quality detection results are input into the algorithm branch decision tree. The branch judgments are performed one by one according to the preset execution order of the multiple branch judgment strategies. After each branch judgment strategy, the image quality enhancement algorithm corresponding to the current branch judgment result is determined. After the judgment is completed, an image quality enhancement strategy composed of at least one of the image quality enhancement algorithms is obtained.

7. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the image quality adjustment method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, is used to implement the image quality adjustment method according to any one of claims 1-5.

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