A learning-based super-resolution reconstruction method for low-bit-rate compressed images

Abstract

The invention discloses a learning-based low bit rate compressed image super-resolution reconstruction method, and the algorithm is divided into an offline part and an online part. In the offline part, the low-resolution image is first compressed with different compression quality parameter values; then the compressed image is filtered, and then the quantized distortion degree of the filtered compressed image is used as a feature, and the filtered LR image is classified according to its degree of distortion. Establish a classification sample library for multiple classes, and then use each type of sample to train the super-resolution model. In the online part, the input image is firstly filtered, and then its compression distortion category is determined. Then, according to the determination result, the sample library and super-resolution model of the corresponding category are selected to realize super-resolution restoration based on learning. Compared with other algorithms, the method of the present invention can adaptively adjust the matching sample library for input LR images with different degrees of distortion, and can effectively solve the impact of block effect distortion on image super-resolution, and directly solve the problem of low bit rate Compared with the super-resolution restoration of the distorted image, the image reconstructed by the method of the present invention has higher subjective and objective quality.

Description

technical field [0001] The invention relates to an image processing method, in particular to a learning-based low-bit-rate compressed image super-resolution reconstruction method. Background technique [0002] High-quality images and videos are increasingly becoming a mainstream demand because of their richer information and more realistic visual experience. However, the higher the quality of the image, the greater the amount of data, which brings a great burden to the storage, transmission, and processing of image information. Affected by factors such as transmission bandwidth and storage space, there is an increasing demand for low-bit compression of video and image content. However, when the JPEG compression rate is high, it usually leads to a decrease in the quality of the reconstructed image after decoding, affecting the subjective quality and automatic analysis of information. Therefore, for highly compressed low-quality images, it is of great theoretical significanc...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve low-resolution images with block effect distortion by using an improved post-processing filtering algorithm and a sample pre-classification method for low-bit-rate compressed images, combined with a learning-based image super-resolution method. Image super-resolution restoration problem

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