Learning-based low-bit-rate compression image super-resolution reconstruction method

Abstract

The invention discloses a learning-based low-bit-rate compression image super-resolution reconstruction method. The learning-based low-bit-rate compression image super-resolution reconstruction method comprises the steps: offline part: firstly compressing low-resolution images by adopting different compression mass parameter values; then filtering the compressed images, taking the quantizing distortion degrees of the filtered compressed images as characteristics, classifying the filtered LR images according to the distortion degrees and establishing a classified sample library, and performing super-resolution model training on all samples respectively. In an online part, the flowing steps are carried out: firstly, filtering input images, then judging a compression distortion class, selecting the sample library and the super-resolution model of the corresponding class according to the judgment result, so as to realize the learning-based super-resolution restoration. Compared with other algorithms, through using the method, the sample library can be self-adaptively regulated so as to match the input LR images according to different distortion degrees, the influence of blocking effect distortion on the super-resolution of the images can be effectively overcome, and compared with the method of directly performing super-resolution restoration on the low-bit-rate distortion images, the images reconstructed by adopting the method has high 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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