A picture quality enhancement method, apparatus, device, medium and product

Abstract

The application discloses a picture quality enhancement method and device, equipment, medium and product, which are applied to the technical field of video coding and decoding, and include the following steps: analyzing code stream data to obtain the distance between a reconstructed frame and a previous instant decoding refresh frame and the level of a pyramid reference frame structure; determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance; calculating the enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient; and performing picture quality enhancement on the reconstructed frame based on the enhancement intensity factor. In this way, the picture quality fluctuation can be reduced, the picture quality enhancement effect can be improved, and the user experience can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of video coding, in particular to a picture quality enhancement method and device, equipment, medium and product. BACKGROUND

[0002] At present, quantization in the video coding process will introduce compression distortion, and after decoding at the playback end, image picture quality enhancement technology will be used to enhance the picture quality. However, the existing picture quality enhancement method is usually global, which causes the frame with good picture quality to become better after enhancement, while the frame with poor picture quality still has a gap with the frame with good picture quality, and even enlarges the gap, aggravates the picture quality fluctuation, and affects the user experience.

[0003] Therefore, how to reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience is a technical problem to be solved at present. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a picture quality enhancement method, device, equipment, medium and product, which can reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience.

[0005] In a first aspect, the present application provides a picture quality enhancement method, comprising: parsing code stream data to obtain a distance between a reconstructed frame and a previous instant decoding refresh frame and a level of a pyramid reference frame structure; determining a weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance; calculating an enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient; and performing picture quality enhancement on the reconstructed frame based on the enhancement intensity factor.

[0006] Optionally, determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level comprises: determining the weight coefficient corresponding to the reconstructed frame based on a first adjustment coefficient corresponding to the distance and a second adjustment coefficient corresponding to the distance and the level.

[0007] Optionally, determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level comprises: using a preset weight coefficient calculation formula, and determining the weight coefficient corresponding to the reconstructed frame based on a first adjustment coefficient corresponding to the distance and a second adjustment coefficient corresponding to the distance and the level, the preset weight coefficient calculation formula being:

[0008] ;

[0009] Wherein, W represents a weight coefficient, L represents the level, D represents the distance, a represents the first adjustment coefficient, and b represents the second adjustment coefficient.

[0010] Optionally, the calculation of the enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient comprises: calculating a basic enhancement factor based on the weight coefficient; and determining the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and a third adjustment coefficient.

[0011] Optionally, the calculation of the basic enhancement factor based on the weight coefficient comprises: calculating the basic enhancement factor based on the weight coefficient and a preset basic enhancement factor calculation formula, the basic enhancement factor calculation formula being:

[0012] ;

[0013] Wherein, represents the basic enhancement factor, and W represents the weight coefficient.

[0014] Optionally, the determination of the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and the third adjustment coefficient comprises: determining the third adjustment coefficient based on the frame type of the reconstructed frame; and determining the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and the third adjustment coefficient.

[0015] In a second aspect, the present application provides a picture quality enhancement device, comprising:

[0016] A bitstream data analysis module is configured to analyze bitstream data to obtain a distance between a reconstructed frame and a previous instant decoding refresh frame and a level of a pyramid reference frame structure.

[0017] A weight coefficient determination module is configured to determine a weight coefficient corresponding to the reconstructed frame based on the distance and the level, wherein the weight coefficient is positively correlated with the level and the distance.

[0018] An intensity factor calculation module is configured to calculate an enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient.

[0019] A picture quality enhancement module is configured to perform picture quality enhancement on the reconstructed frame based on the enhancement intensity factor.

[0020] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein: the memory is configured to save a computer program; and the processor is configured to execute the computer program to implement the picture quality enhancement method described above.

[0021] In a fourth aspect, the present application provides a computer readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the picture quality enhancement method described above.

[0022] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of the picture quality enhancement method described above.

[0023] As can be seen from the above solution, the present application provides a picture quality enhancement method, comprising: parsing code stream data to obtain a distance between a reconstructed frame and a previous instant decoding refresh frame and a level in a pyramid reference frame structure; determining a weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance; calculating an enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient; and performing picture quality enhancement on the reconstructed frame based on the enhancement intensity factor.

[0024] As can be seen, the present application has the following beneficial effects: the enhancement intensity factor corresponding to the reconstructed frame is calculated through the weight coefficient, and the weight coefficient is calculated according to the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, and the weight coefficient is positively correlated with the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, that is, the greater the distance, the greater the weight coefficient, and the higher the level, the greater the weight coefficient. Thus, considering that the higher the level in the pyramid reference frame structure, the greater the distortion of the reference frame itself is accumulated and propagated multiple times, and the greater the distortion, and as the frame moves away from the instant decoding refresh frame, the prediction link becomes longer, and the accumulated distortion gradually increases, the weight coefficient is determined according to the level and the distance from the instant decoding refresh frame, and then the weight coefficient is used to realize the differentiated enhancement of the reconstructed frame, which can reduce picture quality fluctuations, improve picture quality enhancement effect, and further improve user experience.

[0025] Correspondingly, the picture quality enhancement device, equipment, readable storage medium and product provided by the present application also have the above technical effects. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0027] Figure 1 A pyramid reference frame structure diagram is provided for the embodiments of the present application.

[0028] Figure 2 An IDR frame structure diagram provided for an embodiment of the present application;

[0029] Figure 3 A picture quality enhancement method flowchart provided for an embodiment of the present application;

[0030] Figure 4 A picture quality enhancement diagram provided for an embodiment of the present application;

[0031] Figure 5 A picture quality enhancement flowchart provided for an embodiment of the present application;

[0032] Figure 6 A picture quality adjustment process diagram provided for an embodiment of the present application;

[0033] Figure 7 A picture quality enhancement device structure diagram provided for an embodiment of the present application;

[0034] Figure 8 An electronic device structure diagram provided for an embodiment of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0036] Quantization in a video encoding process can introduce compression distortion (blocking effect, blurring, color distortion, etc.), and video encoding usually adopts a pyramid reference frame structure to utilize the temporal redundancy between frames for prediction compression. High-level frames are predicted and encoded by referring to low-level frames (I frames or P frames or B frames), as shown in Figure 1 Figure 1 An pyramid reference frame structure diagram provided for an embodiment of the present application, which can cause accumulation and propagation of compression distortion. In addition, a common strategy of an encoder is to allocate more code rate (use a lower quantization parameter QP) for low-level frames to guarantee quality, and relatively less code rate is allocated for middle-level and top-level frames (or the encoder uses a higher QP to compress them). These factors superimpose each other, and as the reference level becomes deeper, the distortion of the reference frames relied on by the frames has been accumulated and propagated for many times, and the distortion is relatively large, and the image quality will also become poor. Figure 1 The middle t0 is the bottom layer of the pyramid.

[0037] ​In addition, in addition to the influence of the pyramid structure of the reference frame, the distance of the reference frame to the IDR frame is also an important factor. As the distance from the IDR (Instantaneous Decoding Refresh frame) increases, the prediction link becomes longer, the accumulated distortion gradually increases, the picture details become blurred, the blocking effect increases, and the picture quality gradually deteriorates. When the next IDR frame is reached, as shown in Figure 2 Figure 2 An IDR frame structure diagram is provided for the embodiment of the application. The encoder resets the prediction chain, and all previous accumulated distortion is completely removed, the picture is clear at a moment, and the picture quality will appear a fluctuation from good to bad and then to good. The visual performance of this quality periodic fluctuation is usually called breathing effect.

[0038] Considering the influence of the above factors, the quality of the encoded video received by the playback end has quality fluctuations, the consistency of the video quality of the playback end is difficult to guarantee, and the user experience is affected.

[0039] On the other hand, some image quality enhancement techniques of the playback end can greatly help to improve the picture quality, but the picture quality optimization is usually global and does not take into account the picture quality fluctuations caused by encoding, so that the enhanced picture quality of the frame with good picture quality becomes better, while the picture quality of the frame with poor picture quality still has a gap with the frame with good picture quality, and even the gap is enlarged, which aggravates the picture quality fluctuation and affects the user experience.

[0040] Therefore, the application converts the reference structure information of the encoding end into the enhancement decision basis of the playback end, realizes breathing effect elimination and quality smooth transition through quantization of level sensitivity and distance attenuation effect, establishes a technical closed loop of structure perception and dynamic enhancement, and improves the picture quality experience of the user when watching the video in the playback end.

[0041] Referring to Figure 3 The embodiment of the application discloses a picture quality enhancement method, which comprises the following steps:

[0042] Step S11: Analyzing the code stream data to obtain the distance between the reconstructed frame and the previous IDR frame and the level in the pyramid reference frame structure.

[0043] In the embodiment of the application, the code stream data can be analyzed to extract metadata, and the distance between the reconstructed frame and the previous IDR frame and the level in the pyramid reference frame structure are calculated based on the metadata. The extracted metadata is reference relationship metadata, including POC (Picture Order Count), GOP size (Group of Picture size), and frame type, and the frame type can include I frame, P frame, and B frame.

[0044] The calculation formula of the level of the reconstructed frame in the pyramid reference frame structure is:

[0045] ;

[0046] ​Wherein, N is GOP size, p is the relative position of the current frame, i.e. the current reconstructed frame, in the GOP (Group of Pictures), p = poc mod N, gcd(p, N) represents the greatest common divisor of p and N, and log2 is the logarithm function with base 2.

[0047] The calculation formula of the distance between the reconstructed frame and the previous instantaneously decoded refresh frame is as follows: Wherein, poc is the POC value of the current frame, POC value of the previous IDR frame (i.e. the previous instantaneously decoded refresh frame) closest to the current frame.

[0048] Step S12: determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance.

[0049] The embodiments of the present application can determine the weight coefficient corresponding to the reconstructed frame based on the first adjustment coefficient corresponding to the distance and the second adjustment coefficient corresponding to the distance and the level.

[0050] In an optional embodiment, the weight coefficient corresponding to the reconstructed frame is determined based on the first adjustment coefficient corresponding to the distance and the second adjustment coefficient corresponding to the distance and the level by using a preset weight coefficient calculation formula, and the preset weight coefficient calculation formula is as follows:

[0051] ;

[0052] Wherein, W represents the weight coefficient, L represents the level, D represents the distance, a represents the first adjustment coefficient, and b represents the second adjustment coefficient. The level range is an integer [0, 5], D is a non-negative integer, 0≤a, b≤1 and a+b=1.

[0053] Step S13: calculating the enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient.

[0054] The embodiments of the present application can calculate a basic enhancement factor based on the weight coefficient; and determine the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and a third adjustment coefficient.

[0055] In an optional embodiment, the basic enhancement factor is calculated based on the weight coefficient, which includes: calculating the basic enhancement factor based on the weight coefficient and a preset basic enhancement factor calculation formula, and the preset basic enhancement factor calculation formula is as follows:

[0056] ;

[0057] Wherein, wherein, W represents a weight coefficient, and S represents a basic enhancement factor.

[0058] It should be noted that the influence of the pyramid level (L) and the distance (D) from the IDR frame on the quality degradation is nonlinear, the quality degradation of the low L layer is slower, and the quality degradation of the high L layer is faster; the quality is relatively stable when close to the IDR frame (D is small), and the quality deteriorates rapidly after the distance increases. Therefore, an image quality enhancement intensity control factor, i.e., a basic enhancement factor, is designed in the embodiment of the present application to ensure that the enhanced image quality is in a relatively stable state. W is divided into three intervals, W<0.3 is a protection interval, 0.3≤W≤0.7 is a balance interval, and W>0.7 is an enhancement interval. ∈[0.05,1.0].

[0059] In an optional embodiment, the enhancement intensity factor corresponding to the reconstructed frame is determined based on the basic enhancement factor and a third adjustment coefficient, including: determining the third adjustment coefficient based on the frame type of the reconstructed frame; and determining the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and the third adjustment coefficient. In the embodiment of the present application, the third adjustment coefficient corresponding to an I frame is smaller than the third adjustment coefficient corresponding to a P frame, which is smaller than the third adjustment coefficient corresponding to a B frame. For example, the third adjustment coefficient corresponding to an I frame is 0.8, the third adjustment coefficient corresponding to a P frame is 1.0, and the third adjustment coefficient corresponding to a B frame is 1.2. The calculation formula of the enhancement intensity factor is: ; wherein, S is the enhancement intensity factor, is the third adjustment coefficient, and S∈[0.04,1.2].

[0060] Step S14: performing image quality enhancement on the reconstructed frame based on the enhancement intensity factor.

[0061] wherein, the enhancement intensity factor represents the intensity of the image quality enhancement, and the greater the enhancement intensity factor, the greater the intensity of the image quality enhancement. The enhancement intensity factor can be transmitted to a preset image enhancement algorithm in the embodiment of the present application to achieve the enhancement of the image quality.

[0062] It can be seen that, in the embodiments of the present application, the weight coefficient is calculated according to the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, and the weight coefficient and the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure are positively correlated, that is, the greater the distance, the greater the weight coefficient, and the higher the level, the greater the weight coefficient. Therefore, considering that the higher the level in the pyramid reference frame structure, the greater the distortion of the reference frame itself is accumulated and propagated multiple times, and the farther the frame is from the instant decoding refresh frame, the longer the prediction link is, and the accumulated distortion gradually increases, the weight coefficient is determined according to the level and the distance from the instant decoding refresh frame, and then the weight coefficient is used to realize the differentiated enhancement of the reconstructed frame, which can reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience.

[0063] Referring to Figure 4 It can be seen that, in the embodiments of the present application, the weight coefficient is calculated according to the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, and the weight coefficient and the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure are positively correlated, that is, the greater the distance, the greater the weight coefficient, and the higher the level, the greater the weight coefficient. Therefore, considering that the higher the level in the pyramid reference frame structure, the greater the distortion of the reference frame itself is accumulated and propagated multiple times, and the farther the frame is from the instant decoding refresh frame, the longer the prediction link is, and the accumulated distortion gradually increases, the weight coefficient is determined according to the level and the distance from the instant decoding refresh frame, and then the weight coefficient is used to realize the differentiated enhancement of the reconstructed frame, which can reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience.

[0064] In the code stream analysis process, the embodiments of the present application mainly analyze the frame type (I frame / P frame / B frame) of each reconstructed frame, the reference frame pyramid level L where the current frame is located, and the distance D of the current frame from the nearest previous IDR frame. The pyramid level L where the current frame is located is calculated according to the following formula:

[0065] ;

[0066] Wherein: N is the GOP size, p is the relative position of the current frame in the GOP, p = poc mod N, gcd(p, N) represents the greatest common divisor of p and N, and log2 is the logarithm function with base 2. The distance D of the current frame from the nearest previous IDR frame is calculated according to the following formula: ; wherein: poc is the POC value of the current frame, is the POC value of the nearest previous IDR frame from the current frame.

[0067] Further, referring to Figure 5 It can be seen that, in the embodiments of the present application, the weight coefficient is calculated according to the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, and the weight coefficient and the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure are positively correlated, that is, the greater the distance, the greater the weight coefficient, and the higher the level, the greater the weight coefficient. Therefore, considering that the higher the level in the pyramid reference frame structure, the greater the distortion of the reference frame itself is accumulated and propagated multiple times, and the farther the frame is from the instant decoding refresh frame, the longer the prediction link is, and the accumulated distortion gradually increases, the weight coefficient is determined according to the level and the distance from the instant decoding refresh frame, and then the weight coefficient is used to realize the differentiated enhancement of the reconstructed frame, which can reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience.Figure 5 The diagram illustrates the weight calculation and dynamic image enhancement control process for the current frame to be processed. The critical weight calculation, based on the pyramid level (L) and IDR distance (D), is as follows:

[0068] ;

[0069] Where L is the pyramid level, ranging from an integer [0, 5]. D is the distance to the nearest IDR frame, and is a non-negative integer. α and β are adjustment coefficients (0 ≤ α, β ≤ 1 and α + β = 1).

[0070] Furthermore, dynamic enhancement control is implemented by calculating an enhancement intensity factor. The effects of pyramid level (L) and distance from the IDR frame (D) on image quality degradation are non-linear; lower L levels experience slower degradation, while higher L levels experience faster degradation. Image quality is relatively stable when close to the IDR frame (smaller D), but deterioration accelerates with increasing distance. Considering this characteristic, this application's embodiments design an image quality enhancement intensity control factor to ensure that the enhanced image quality remains relatively stable.

[0071] ;

[0072] Divide W into three intervals: W<0.3 is the protection interval, 0.3≤W≤0.7 is the equilibrium interval, and W>0.7 is the enhancement interval. .

[0073] Select the adjustment factor based on the frame type. The adjustment factor is 0.8 for I-frames, 1.0 for P-frames, and 1.2 for B-frames. Final enhancement intensity factor. Where S∈[0.04,1.2], the final enhancement factor is passed to the image enhancement algorithm to adjust the image quality. See also Figure 6 As shown, Figure 6 This is a schematic diagram illustrating an image quality adjustment process provided in an embodiment of this application. For the frame to be processed, an enhancement intensity factor is calculated, an image quality enhancement algorithm is invoked, and rendering and display are performed.

[0074] The embodiment of the application combines video coding reference structure information to enhance the picture quality, fully considers the prediction dependence factor caused by the video coding reference frame structure, and effectively solves the picture quality fluctuation problem caused by the reference frame dependence. The reference pyramid structure information (level depth L, IDR distance D) of video coding is converted into a playback end enhancement decision basis, breaking the blindness of traditional algorithm global enhancement, and realizing coding and decoding collaborative optimization. The influence of the pyramid structure information (level depth L, IDR frame distance D) on the propagation distortion is mathematically modeled, the distortion is quantified, and the weight factor used to control the picture quality enhancement strength is calculated. In the dynamic enhancement control process of the picture quality, in addition to considering the frame structure information, the frame type information is also considered. The dynamic control of the playback end picture quality enhancement is realized. Compared with the global uniform strength enhancement, the dynamic control helps to reduce the overall complexity and reduce the power consumption of the end side.

[0075] In this way, the picture quality fluctuation problem of different levels of video frames caused by the video coding pyramid structure, and the picture quality decay caused by the lengthening of the reference link distance from the IDR frame, and the periodic picture quality fluctuation (breathing effect) problem are solved. The traditional playback end enhancement algorithm ignores the level difference of the reference frame, has the dual defects of over-processing of low-level frames and insufficient compensation of high-level frames, expands the picture quality fluctuation, and affects the user experience. The embodiment of the application can break the information fragmentation dilemma of coding and playback end picture optimization, obtain the reference relationship metadata (such as frame type, reference level depth, distance from IDR frame) in the video stream by analysis, and construct a quantitative driving adaptive enhancement model. Based on the reference level key weight dynamic regulation processing intensity: strengthening noise reduction and blocking effect for high-level / distance frames, and inhibiting over-enhancement for low-level / near-distance frames, thereby realizing distortion directional repair and quality smooth transition at the playback end, completely eliminating the breathing effect, and achieving coding structure perception and playback accurate compensation collaborative closed loop.

[0076] Referring to Figure 7 The application provides a picture quality enhancement device, which comprises:

[0077] A code stream data analysis module 11 is configured to analyze the code stream data to obtain the distance between the reconstructed frame and the previous instant decoding refresh frame and the level of the pyramid reference frame structure.

[0078] A weight coefficient determination module 12 is configured to determine the weight coefficient corresponding to the reconstructed frame based on the distance and the level. The weight coefficient is positively correlated with the level and the distance.

[0079] An intensity factor calculation module 13 is configured to calculate the enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient.

[0080] The quality enhancement module 14 is configured to perform quality enhancement on the reconstructed frame based on the enhancement intensity factor.

[0081] In an optional implementation, the weight coefficient determination module 12 is specifically configured to determine the weight coefficient corresponding to the reconstructed frame based on the first adjustment coefficient corresponding to the distance and the distance, and the second adjustment coefficient corresponding to the level and the level.

[0082] In an optional implementation, the weight coefficient determination module 12 is specifically configured to determine the weight coefficient corresponding to the reconstructed frame based on the first adjustment coefficient corresponding to the distance and the distance, and the second adjustment coefficient corresponding to the level and the level, by using a preset weight coefficient calculation formula.

[0083] ;

[0084] wherein, W represents the weight coefficient, L represents the level, D represents the distance, a represents the first adjustment coefficient, and b represents the second adjustment coefficient.

[0085] In an optional implementation, the intensity factor calculation module 13 can include:

[0086] The base enhancement factor calculation submodule is configured to calculate a base enhancement factor based on the weight coefficient.

[0087] The enhancement intensity factor calculation submodule is configured to determine the enhancement intensity factor corresponding to the reconstructed frame based on the base enhancement factor and a third adjustment coefficient.

[0088] In an optional implementation, the base enhancement factor calculation submodule is specifically configured to calculate the base enhancement factor based on the weight coefficient and a preset base enhancement factor calculation formula.

[0089] ;

[0090] wherein, represents the base enhancement factor, and W represents the weight coefficient.

[0091] In an optional implementation, the enhancement intensity factor calculation submodule is specifically configured to determine the third adjustment coefficient based on the frame type of the reconstructed frame, and determine the enhancement intensity factor corresponding to the reconstructed frame based on the base enhancement factor and the third adjustment coefficient.

[0092] It can be seen that, in the embodiment of the application, the weight coefficient is calculated according to the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure, and the weight coefficient and the distance between the reconstructed frame and the previous instant decoding refresh frame and the level in the pyramid reference frame structure are positively correlated, that is, the greater the distance, the greater the weight coefficient, and the higher the level, the greater the weight coefficient. Thus, considering that the higher the level in the pyramid reference frame structure, the greater the distortion of the reference frame itself is accumulated and propagated multiple times, and the greater the distortion and the longer the prediction link and the greater the accumulated distortion as the frame moves away from the instant decoding refresh frame, the weight coefficient is determined according to the level and the distance from the instant decoding refresh frame, and then the weight coefficient is used to realize the differentiated enhancement of the reconstructed frame, which can reduce the picture quality fluctuation, improve the picture quality enhancement effect, and further improve the user experience.

[0093] Referring to Figure 8 As shown in the figure, the embodiment of the application discloses an electronic device 20, comprising a processor 21 and a memory 22; wherein the memory 22 is used to save a computer program; the processor 21 is used to execute the computer program, and the picture quality enhancement method disclosed in the foregoing embodiments.

[0094] For the specific process of the picture quality enhancement method, reference can be made to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0095] In addition, the memory 22 as a carrier for storing resources can be a read-only memory, a random access memory, a magnetic disk or an optical disk, and the storage mode can be temporary storage or permanent storage.

[0096] In addition, the electronic device 20 further comprises a power supply 23, a communication interface 24, an input / output interface 25 and a communication bus 26; wherein the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the application, which will not be limited here; the input / output interface 25 is used to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which will not be limited here.

[0097] Further, the embodiment of the application further discloses a computer readable storage medium for saving a computer program, wherein the computer program is executed by a processor to realize the picture quality enhancement method disclosed in the foregoing embodiments.

[0098] For the specific process of the picture quality enhancement method, reference can be made to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0099] Further, the embodiments of the present application also disclose a computer program product, comprising computer programs / instructions, which realize the steps of the aforementioned picture quality enhancement method when executed by a processor.

[0100] The specific process of the aforementioned picture quality enhancement method can refer to the corresponding content disclosed in the aforementioned embodiments, and will not be repeated here.

[0101] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can refer to the method part.

[0102] The steps of the method or algorithm described in combination with the embodiments disclosed herein can be implemented directly with hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0103] The above describes in detail the picture quality enhancement method, device, equipment, medium and product provided by the present application. The principles and implementation manners of the present application are described by applying specific examples. The above embodiment description is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed; and the above description should not be understood as a limitation of the present application.

Claims

1. An image quality enhancement method characterized by comprising: The method comprises the following steps: parsing the code stream data to obtain a distance between a reconstructed frame and a previous instant decoding refresh frame and a level in a pyramid reference frame structure; determining a weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance; calculating an enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient; performing quality enhancement on the reconstructed frame based on the enhancement intensity factor; wherein the step of calculating the enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient comprises the following steps: ; denotes the base enhancement factor, W denotes the weight coefficient; determining a third adjustment coefficient based on the frame type of the reconstructed frame; determining the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and the third adjustment coefficient.

2. The image quality enhancement method of claim 1, wherein The step of determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level comprises the following steps: determining the weight coefficient corresponding to the reconstructed frame based on a first adjustment coefficient corresponding to the distance and a second adjustment coefficient corresponding to the distance and the level.

3. The image quality enhancement method of claim 2, wherein The step of determining the weight coefficient corresponding to the reconstructed frame based on the distance and the level comprises the following steps: using a preset weight coefficient calculation formula and determining the weight coefficient corresponding to the reconstructed frame based on the first adjustment coefficient corresponding to the distance and the second adjustment coefficient corresponding to the distance and the level, wherein the preset weight coefficient calculation formula is: ； wherein W represents the weight coefficient, L represents the level, D represents the distance, a represents the first adjustment coefficient, and b represents the second adjustment coefficient.

4. An image quality enhancement apparatus characterized by comprising: The method comprises the following steps: a code stream data parsing module for parsing the code stream data to obtain a distance between a reconstructed frame and a previous instant decoding refresh frame and a level in a pyramid reference frame structure; a weight coefficient determining module for determining a weight coefficient corresponding to the reconstructed frame based on the distance and the level; wherein the weight coefficient is positively correlated with the level and the distance; an intensity factor calculating module for calculating an enhancement intensity factor corresponding to the reconstructed frame based on the weight coefficient; a quality enhancement module for performing quality enhancement on the reconstructed frame based on the enhancement intensity factor; wherein the intensity factor calculating module specifically comprises: a basic enhancement factor calculating submodule for calculating a basic enhancement factor based on the weight coefficient and a preset basic enhancement factor calculation formula, wherein the basic enhancement factor calculation formula is: ; denotes the base enhancement factor, W denotes the weight coefficient; an enhancement intensity factor calculating submodule for determining a third adjustment coefficient based on the frame type of the reconstructed frame and determining the enhancement intensity factor corresponding to the reconstructed frame based on the basic enhancement factor and the third adjustment coefficient.

5. An electronic device, comprising: The method comprises a memory and a processor, wherein: the memory is used to save a computer program; the processor is used to execute the computer program to realize the quality enhancement method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, A computer program product for saving a computer program, wherein the computer program, when executed by a processor, implements the image quality enhancement method according to any one of claims 1 to 3.

7. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by a processor, implement the steps of the image quality enhancement method according to any one of claims 1 to 3.

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