Method, system, device and medium for rate control of end-to-end ai image compression

Abstract

The application discloses a kind of end-to-end AI image compression code rate control method, system, equipment and medium, utilize the characteristics that end-to-end image compression model can infinitely interpolate between highest and lowest code point, interpolate more code points, to obtain rate control table (Rate Control Table, abbreviated as RCT), and according to super priori and context model estimation maximum and minimum code rate, select the closest code point from RCT to achieve the effect of code rate control, so that a reliable transmission bandwidth can be set in the case of limited bandwidth.The application does not need to retrain model, it is a very suitable code rate control method for deep learning end-to-end image compression, and error is smaller.

Description

Technical Field

[0001] This invention relates to the field of image compression, and more particularly to a bitrate control method, system, device, and medium for end-to-end AI image compression. Background Technology

[0002] Currently, deep learning-based image encoding and decoding models in academia have surpassed the compression performance of traditional H.266, H.265, WEBP, and JPEG in terms of PSNR (Peak Signal-to-Noise Ratio), MS-SSIM (Multi-Scale Structural Similarity), and human subjective compression performance. Deep learning-based end-to-end image compression schemes hold great potential. Furthermore, traditional compression algorithms typically use bitstream truncation for bitrate control, resulting in poor reconstructed image quality. Existing deep learning models have multi-bitrate mechanisms, but their bitrate control is poorly designed based on reconstructed image quality, leading to significant differences in compression ratios (even reaching tens of times) for the same bit point. This makes it difficult to set a maximum transmission bandwidth (minimum compression ratio / maximum bitrate is difficult to control) and results in inconsistent reconstructed image quality during practical deployment of deep learning models. Summary of the Invention

[0003] The purpose of this invention is to provide a bitrate control method, system, device and medium for end-to-end AI image compression, which can control the compression bitrate near a specified bitrate with a small error, and achieve similar objective indicators and better subjective effects as the original framework, thereby ensuring the quality of the reconstructed image.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A bitrate control method for end-to-end AI image compression includes:

[0006] Obtain the multi-bitrate model in the image compression model, select the highest and lowest bit points, and use the weights corresponding to the highest and lowest bit points to uniformly interpolate multiple bit points between the highest and lowest bit points to obtain the weights corresponding to each bit point. Store all the interpolated bit points and the weights corresponding to the highest and lowest bit points as a bitrate control table.

[0007] The original image data is input into the image compression model, and the primary features of the image are extracted through the convolutional network in the image compression model. Then, based on the super-prior probability model and the context prediction model in the image compression model, the maximum and minimum pixel depths are calculated.

[0008] By combining the maximum and minimum pixel depths with the input target pixel depth, the index of the required code point in the bitrate control table is calculated, and then the corresponding weight is determined by querying the bitrate control table.

[0009] The retrieved weights are multiplied by the primary features of the image and quantized by the quantization module. Then, the image is entropy encoded and decoded by the super-prior probability model and the context prediction model. Finally, the image is obtained by passing the deconvolution network in the image compression model.

[0010] A bitrate control system for end-to-end AI image compression, comprising:

[0011] The bitrate control table construction and bit point storage unit is used to obtain the multi-bitrate model in the image compression model, select the highest and lowest bit points, and use the weights corresponding to the highest and lowest bit points to uniformly interpolate multiple bit points in the highest and lowest bit points to obtain the weights corresponding to each bit point. All interpolated bit points and the weights corresponding to the highest and lowest bit points are stored as a bitrate control table.

[0012] The maximum and minimum pixel depth calculation unit is used to input the original image data into the image compression model, extract the primary features of the image through the convolutional network in the image compression model, and then calculate the maximum and minimum pixel depth based on the super-prior probability model and the context prediction model in the image compression model.

[0013] The index calculation and code point determination unit is used to calculate the index of the required code point in the code rate control table by combining the maximum and minimum pixel depths and the input target pixel depth, and then query the code rate control table to determine the corresponding weight.

[0014] The rate control and reconstruction unit is used to multiply the queried weights with the primary features of the image, and then quantize them through the quantization module. After that, the super-prior probability model and the context prediction model perform entropy encoding and entropy decoding. Finally, the reconstructed image is obtained through the deconvolution network in the image compression model.

[0015] A processing device includes: one or more processors; and a memory for storing one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0017] A readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0018] As can be seen from the technical solution provided by this invention, by utilizing the characteristic of the end-to-end image compression model to interpolate infinitely between the highest and lowest code points, a large number of code points can be interpolated to obtain a Rate Control Table (RCT). Based on the prior and context model, the maximum and minimum code rates are estimated, and the closest code point is selected from the RCT to achieve the effect of rate control. This allows for the setting of a reliable transmission bandwidth even with limited bandwidth. This invention does not require model retraining, making it a highly suitable rate control method for deep learning-based end-to-end image compression with relatively small errors. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic diagram illustrating a bitrate control method for end-to-end AI image compression provided in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a bitrate control system for end-to-end AI image compression provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of a processing device provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0024] First, the following explanations are provided for the terms that may be used in this article:

[0025] The terms “including,” “comprising,” “containing,” “having,” or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example, “including a technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction conditions, processing conditions, parameter, algorithm, signal, data, product or article of manufacture, etc.)” should be interpreted as including not only the expressly listed technical feature element, but also other technical feature elements that are not expressly listed and are well-known in the art.

[0026] The term "composed of" excludes any technical features not expressly listed. When used in a claim, it closes the claim to exclude all technical features other than those expressly listed, except for associated conventional impurities. If the term appears only in a clause of a claim, it limits the claim to the elements expressly listed in that clause; elements recited in other clauses are not excluded from the overall claim.

[0027] Unless otherwise explicitly specified or limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this document according to the specific circumstances.

[0028] The following provides a detailed description of the bitrate control method, system, device, and medium for end-to-end AI image compression provided by this invention. Contents not described in detail in the embodiments of this invention are prior art known to those skilled in the art. Where specific conditions are not specified in the embodiments of this invention, conventional conditions in the art or conditions recommended by the manufacturer shall apply. Where the manufacturers of reagents or instruments used in the embodiments of this invention are not specified, they are all conventional products that can be purchased commercially.

[0029] Example 1

[0030] This invention provides a bitrate control method for end-to-end AI image compression, such as... Figure 1 As shown, it mainly includes the following steps:

[0031] Step 1: Obtain the multi-bitrate model in the image compression model, select the highest and lowest bit points, and use the weights corresponding to the highest and lowest bit points to uniformly interpolate multiple bit points between the highest and lowest bit points to obtain the weight corresponding to each bit point. Store all the interpolated bit points and the weights of the highest and lowest bit points as a bitrate control table.

[0032] In this embodiment of the invention, the image compression model mainly includes: a convolutional network, a quantization module, a super-prior probability model, a context prediction model, and a deconvolutional network. After training the image compression model, a corresponding multi-bitrate model can be obtained. The training scheme for the image compression model can refer to conventional techniques, which will not be elaborated upon in this invention.

[0033] Those skilled in the art will understand that the code point is the compression level of the image compression model. Different code points correspond to different compression bitrates. The compression bitrate is the actual size of the compressed data and is related to the compression factor. The image compression model is a multi-bitrate model, which means that multiple different compression levels are allowed.

[0034] In this embodiment of the invention, the multi-rate model has multiple code points, and the multi-rate effect is achieved by multiplying the features with different weights. The highest and lowest code points are selected, and (N-1) code points are uniformly interpolated between the highest and lowest code points. A total of N+1 code points and their corresponding weights are stored in a table to obtain the rate control table.

[0035] The interpolation method is expressed as:

[0036] w new =αw low +(1-α)w high

[0037] Among them, w new w represents the weight corresponding to a code point interpolated. low w represents the weight corresponding to the lowest code point. high The value of N represents the weight corresponding to the highest code point, and α represents the interpolation coefficient. The value of α gradually decreases according to the order of the interpolated code points from low to high. That is, the larger N is, the smaller the value of α becomes. The number of N can be set according to the actual situation or experience.

[0038] Step 2: Input the original image data into the image compression model, extract the primary features of the image through the convolutional network in the image compression model, and then calculate the maximum and minimum pixel depths based on the super-prior probability model and context prediction model in the image compression model.

[0039] In this embodiment of the invention, the dimensions of the input raw image data can be H, W, 3, where H and W represent the image height and width, respectively, and 3 represents the number of channels (e.g., three RGB channels). The dimensions of the primary image features can be H / s, W / s, C, where C is the number of feature channels, s is the downsampling factor of the convolutional network, and the convolutional network is 1. Figure 1 The encoder in the text.

[0040] In this embodiment of the invention, the primary features of the image are quantized by the quantization module in the image compression model, and then the maximum and minimum entropy of the quantized features are calculated by the hyperprior probability model and the context prediction model in the image compression model. The maximum and minimum pixel depth (bits per pixel, abbreviated as bpp) are calculated using the maximum and minimum entropy of the quantized features.

[0041] Step 3: Combine the maximum and minimum pixel depths with the input target pixel depth to calculate the index of the required code point in the bitrate control table, and then query the bitrate control table to determine the corresponding weight.

[0042] In this embodiment of the invention, the method for calculating the index of the required code point in the rate control table is expressed as follows:

[0043]

[0044] Where, index represents the index of the calculated required code point in the bitrate control table (desired index), which corresponds to a certain code point in the bitrate control table, target bpp represents the input target pixel depth, maxbpp represents the maximum pixel depth, minbpp represents the minimum pixel depth, and N-1 represents the number of all code points obtained by interpolation.

[0045] Step 4: Multiply the retrieved weights by the primary features of the image, quantize them using the quantization module, entropy encoding and decoding are performed by the super-prior probability model and the context prediction model, and finally the reconstructed image is obtained through the deconvolution network in the image compression model.

[0046] The above-described scheme provided in this invention utilizes the characteristic of end-to-end image compression models that can interpolate infinitely between the highest and lowest code points to obtain a large number of code points, thereby obtaining a Rate Control Table (RCT). Based on the prior and context model, the maximum and minimum code rates are estimated, and the closest code point is selected from the RCT to achieve the effect of rate control. This invention does not require retraining the model, making it a highly suitable rate control method for deep learning-based end-to-end image compression with relatively small errors.

[0047] Through the above description of the embodiments, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.), including several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0048] Example 2

[0049] This invention also provides a bitrate control system for end-to-end AI image compression, which is mainly based on the method provided in the foregoing embodiments, such as... Figure 2 As shown, the system mainly includes:

[0050] The bitrate control table construction and bit point storage unit is used to obtain the multi-bitrate model in the image compression model, select the highest and lowest bit points, and use the weights corresponding to the highest and lowest bit points to uniformly interpolate multiple bit points in the highest and lowest bit points to obtain the weights corresponding to each bit point. All interpolated bit points and the weights corresponding to the highest and lowest bit points are stored as a bitrate control table.

[0051] The maximum and minimum pixel depth calculation unit is used to input the original image data into the image compression model, extract the primary features of the image through the convolutional network in the image compression model, and then calculate the maximum and minimum pixel depth based on the super-prior probability model and the context prediction model in the image compression model.

[0052] The index calculation and code point determination unit is used to calculate the index of the required code point in the code rate control table by combining the maximum and minimum pixel depths and the input target pixel depth, and then query the code rate control table to determine the corresponding weight.

[0053] The rate control and reconstruction unit is used to multiply the queried weights with the primary features of the image, and then quantize them through the quantization module. After that, the super-prior probability model and the context prediction model perform entropy encoding and entropy decoding. Finally, the reconstructed image is obtained through the deconvolution network in the image compression model.

[0054] In this embodiment of the invention, the interpolation method is expressed as follows:

[0055] w new =αw low +(1-α)w high

[0056] Among them, w new w represents the weight corresponding to a code point interpolated. low w represents the weight corresponding to the lowest code point. high This represents the weight corresponding to the highest code point, and α represents the interpolation coefficient.

[0057] In this embodiment of the invention, the calculation of the maximum and minimum pixel depths based on the super-prior probability model and context prediction model in the image compression model includes:

[0058] The primary features of the image are quantized by the quantization module in the image compression model. Then, the maximum and minimum entropy of the quantized features are calculated by the super-prior probability model and the context prediction model in the image compression model. Finally, the maximum and minimum pixel depths are calculated using the maximum and minimum entropy of the quantized features.

[0059] In this embodiment of the invention, the method of calculating the index of the required code point in the code rate control table by combining the maximum and minimum pixel depths and the input target pixel depth is expressed as follows:

[0060]

[0061] Where index represents the index of the calculated required code point in the rate control table, target bpp represents the input target pixel depth, maxbpp represents the maximum pixel depth, minbpp represents the minimum pixel depth, and N-1 represents the number of all code points obtained by interpolation.

[0062] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0063] Example 3

[0064] The present invention also provides a processing device, such as Figure 3 As shown, it mainly includes: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the foregoing embodiments.

[0065] Furthermore, the processing device also includes at least one input device and at least one output device; in the processing device, the processor, memory, input device, and output device are connected via a bus.

[0066] In this embodiment of the invention, the specific types of the memory, input device, and output device are not limited; for example:

[0067] Input devices can be touchscreens, image acquisition devices, physical buttons, or mice, etc.

[0068] The output device can be a display terminal;

[0069] The memory can be random access memory (RAM) or non-volatile memory, such as disk storage.

[0070] Example 4

[0071] The present invention also provides a readable storage medium storing a computer program that, when executed by a processor, implements the method provided in the foregoing embodiments.

[0072] In this embodiment of the invention, the readable storage medium is a computer-readable storage medium and can be disposed in the aforementioned processing device, for example, as a memory in the processing device. Furthermore, the readable storage medium can also be any medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0073] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for rate control of end-to-end AI image compression, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The method of claim 1, wherein the method further comprises: The method comprises the following steps: ； wherein, represents the weight corresponding to the lowest code point, represents the weight corresponding to the lowest code point, represents the weight corresponding to the highest code point, represents the interpolation coefficient.

3. The method of claim 1, wherein the method further comprises: The method comprises the following steps: The method comprises the following steps:

4. The method of claim 1, wherein the method further comprises: The method comprises the following steps: ； wherein, represents the index of the calculated required code point in the code rate control table, represents the input target pixel depth, represents the maximum pixel depth, represents the minimum pixel depth, represents the number of all code points obtained by interpolation.

5. A rate control system for end-to-end AI image compression, the system comprising: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

6. The rate control system for end-to-end AI image compression of claim 5, wherein, The method comprises the following steps: ； wherein, denotes the weight corresponding to the interpolated codepoint, denotes the weight corresponding to the lowest codepoint, denotes the weight corresponding to the highest codepoint, denotes the interpolation coefficient.

7. 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8. The rate control system for end-to-end AI image compression of claim 5, wherein, The method for calculating the index of the required code point in the code rate control table by combining the maximum and minimum pixel depths and the input target pixel depth is represented as: ； wherein, represents the index of the calculated required code point in the code rate control table, represents the input target pixel depth, represents the maximum pixel depth, represents the minimum pixel depth, represents the number of all code points obtained by interpolation.

9. A processing device, characterized by The method comprises the following steps: one or more processors; a memory for storing one or more programs; wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.

10. A readable storage medium, storing a computer program, characterized in that, The computer program, when executed by the processor, implements the method of any one of claims 1-4.

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