Image processing method, device and equipment and computer readable storage medium

Abstract

The invention provides an image processing method, device and equipment and a computer readable storage medium. The method comprises the steps of obtaining a to-be-processed image, and performing feature extraction on the to-be-processed image to obtain image features of the to-be-processed image; determining a control vector and a compression degree coefficient based on the image features; determining a target exit stage of the trained image enhancement sub-model based on the compression degree coefficient and a model structure of the trained image enhancement sub-model; based on the image feature and the control vector, obtaining an image processing result of the trained image enhancement sub-model when the target exit stage is reached; and outputting the image processing result. Through the invention, the exit stage of image processing can be determined in a self-adaptive mode, and the image processing efficiency is improved.

Description

technical field [0001] The present application relates to image processing technologies, and in particular to an image processing method, device, equipment, and computer-readable storage medium. Background technique [0002] At present, with the development of terminal technology, the pixels of image acquisition devices are getting higher and higher, so the size of images or video files collected by the image acquisition devices is also increasing accordingly. In order to save transmission bandwidth and storage space, JPG compression technology is widely used. When the user performs JPG compression on the image, the image will be compressed to different degrees by using different compression ratios according to actual needs such as network conditions. Different compression rates will bring different degrees of block effect, ringing effect and blur, etc., resulting in different degrees of image quality degradation. At present, the image decompression effect enhancement algo...

AI Technical Summary

Problems solved by technology

Different compression rates will bring different degrees of block effects, ringing effects and blurring, etc., resulting in different degrees of image quality degradation

At present, the image decompression effect enhancement algorithm based on deep learning has achieved good results. However, a stable network will inevitably bring a small amount of loss of detail while removing the compression effect.

Because simply using a wide range of JPG compression coefficient pictures to train the model, a general-purpose network that can handle different compression situations is obtained. Since the network can only implement a deterministic mapping, it often cannot achieve the optimal in each situation. The application benefit is too small; while analyzing the quality of the image separately, training multiple networks for different compression situations is inefficient and consumes a lot of resources, and for the currently processed image, the compression factor is usually unknown

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