JP-2026076352-A - Encoding method and apparatus, decoding method and apparatus, device, storage medium, and computer program product

JP2026076352AJP 2026076352 AJP2026076352 AJP 2026076352AJP-2026076352-A

Abstract

[Problem] To provide an encoding method and apparatus, a decoding method and apparatus, a device, a storage medium, and a computer program product. [Solution] When probability estimation is performed in the encoding method, the probability distribution of the unquantized image features is estimated based on the hyperplier features of the unquantized image features via a first probability distribution estimation network, and then the probability distribution of the quantized image features is obtained through quantization. Alternatively, the probability distribution of the quantized image features is directly estimated based on the hyperplier features of the unquantized image features via a second probability distribution estimation network (a network obtained by performing a simple operation on the network parameters of the first probability distribution estimation network). [Selection Diagram] Figure 4

Inventors

マオ，ジュエ
ジャオ，イン
ユイ，ドーァチュエン
ジャーン，リエン

Assignees

華為技術有限公司

Dates

Publication Date: 20260511
Application Date: 20260218
Priority Date: 20220310

Claims (9)

An encoding method, wherein the encoding method is: The steps include determining the unquantized image features of the image to be encoded; The steps include determining the hyperplier features of the aforementioned unquantized image features; A step of determining a first probability distribution parameter based on the hyperplier feature via a probability distribution estimation network, wherein the first probability distribution parameter represents the probability distribution of the unquantized image feature; A step of quantizing the unquantized image features based on a first quantization step to obtain quantized image features, and a step of quantizing the first probability distribution parameter based on the first quantization step to obtain a second probability distribution parameter; The process includes the steps of encoding the hyperplier features into a bitstream based on a pre-configured third probability distribution parameter, and encoding the quantized image features into the bitstream based on a second probability distribution parameter, method.
Determining the hyperplier features of the aforementioned unquantized image features is: This includes inputting the aforementioned unquantized image features into a hyperencoder network to obtain the aforementioned hyperplier features, The method according to claim 1.
Determining the hyperplier features of the aforementioned unquantized image features is: Based on the first quantization step, the quantized image features are dequantized to obtain the dequantized image features; This includes inputting the inversely quantized image features into a hyperencoder network to obtain the hyperplier features, The method according to claim 1.
Determining the first probability distribution parameter based on the hyperplier features via the probability distribution estimation network is: The steps include: inputting the inversely quantized image features into a context network to obtain context features of the inversely quantized image features; The steps include determining a first plier feature based on the hyperplier feature; The process includes the steps of inputting the first plier feature and the context feature into the probability distribution estimation network to obtain the first probability distribution parameter, The method according to claim 3.
Determining the first probability distribution parameter based on the hyperplier features via the probability distribution estimation network is: The quantized image features are input into a context network to obtain the context features of the quantized image features; Based on the aforementioned hyperplier feature, the first plier feature is determined; Based on the second quantization step, the first plier feature is quantized to obtain the second plier feature; This includes inputting the second plier feature and the context feature into the probability distribution estimation network to obtain the first probability distribution parameter, The method according to any one of claims 1 to 2.
One or more processors; An encoding device comprising a computer-readable storage medium coupled to the processor and storing a program for execution by the processor, wherein the program, when executed by the processor, enables the encoding device to perform the method according to any one of claims 1 to 5. Encoding device.
An encoding device having a processing circuit configured to perform the method described in any one of claims 1 to 5.
A computer-readable storage medium storing a computer program that, when executed by a computer or processor, enables the computer or processor to perform the method described in any one of claims 1 to 5.
A computer-readable storage medium having a bitstream obtained according to the method described in any one of claims 1 to 5.

Description

This application claims priority to Chinese Patent Application No. 202210234190.8, filed on 10 March 2022, entitled “Encoding Method and Apparatus, Decoding Method and Apparatus, Device, Storage Medium, and Computer Program Product,” which is incorporated herein by reference in its entirety. Technical Field This application relates to the field of encoding and decoding technologies, and more particularly to encoding methods and apparatuses, decoding methods and apparatuses, devices, storage media, and computer program products. Image compression technology enables the effective transmission and storage of image information, playing a crucial role in the media age where the types and amounts of image information are increasing. Image compression technology includes image encoding and decoding. Encoding and decoding performance reflects image quality and is a factor that must be considered in image compression technology. In the encoding process of related technologies, image features y are extracted via an image feature extraction network. Image features y are then quantized based on a quantization step q to obtain image features ys. Image features ys are input to a hyperencoder network to determine hyperplier features zs, which are then encoded into a bitstream via entropy encoding. Entropy decoding is performed on the hyperplier features zs in the bitstream to obtain hyperplier features zs', and based on hyperplier features zs', the probability distribution parameters of image features ys are obtained via a probability distribution estimation network. Based on the probability distribution parameters of image features ys, image features ys are encoded into a bitstream via entropy encoding. The decoding process is related to the encoding process. Most image compression is implemented using quantization operations, and these quantization operations significantly impact encoding and decoding performance. However, quantization operations in the encoding and decoding processes must match the bitrate. In multi-bitrate scenarios, different quantization steps are typically required in the encoding and decoding processes to match different bitrates. However, different quantization steps result in significant differences in the numerical range of image features ys obtained through quantization. To obtain a probability distribution estimation network for estimating the probability distribution parameters of image features ys at different bitrates, the probability distribution estimation network must be trained using image features ys with different numerical ranges. However, the numerical range of image features ys changes significantly at different bitrates, making it difficult to train the probability distribution estimation network, resulting in unstable network training and difficulty in obtaining a probability distribution estimation network with good performance through training. As a result, encoding and decoding performance is affected. This is a diagram of an implementation environment according to one embodiment of the present invention. This is a diagram of another implementation environment according to one embodiment of the present invention. This is a diagram of yet another implementation environment according to one embodiment of the present invention. This is a flowchart of an encoding method according to one embodiment of the present invention. This is a diagram showing the structure of an image feature extraction network according to one embodiment of the present invention. This is a flowchart of an encoding and decoding method according to one embodiment of the present invention. This is a flowchart of another encoding and decoding method according to one embodiment of the present invention. This is a flowchart of yet another encoding and decoding method according to one embodiment of the present invention. This is a flowchart of yet another encoding and decoding method according to one embodiment of the present invention. This is a flowchart of yet another encoding and decoding method according to one embodiment of the present invention. This is a flowchart of another encoding method according to one embodiment of the present invention. This is a flowchart of a decoding method according to one embodiment of the present invention. This is a flowchart of another decoding method according to one embodiment of the present invention. This is a structural diagram of an encoding device according to one embodiment of the present invention. This is a structural diagram of a decoding device according to one embodiment of the present invention. This is a structural diagram of another encoding device according to one embodiment of the present invention. This is a structural diagram of another decoding device according to one embodiment of the present invention. This is a block diagram of an encoding and decoding device according to one embodiment of the present invention. Embodiments of the present invention w