Image compression method and device

Abstract

An image compression method disclosed by the present invention comprises the steps of separately evaluating the image quality of n compressed images obtained by separately adopting n sets of different quality factors to carry out the joint photographic experts group (JPEG) image compression on an original image, and obtaining n sets of quality evaluation scores; utilizing the n sets of quality evaluation scores and the corresponding quality factors to carry out the function fitting to obtain a function relation between the quality evaluation scores and the quality factors; utilizing the function relation between the quality evaluation scores and the quality factors to calculate a target quality factor corresponding to a target quality score; determining an optimal quality factor according to the target quality factor, and utilizing the optimal quality factor to carry out the JPEG image compression on the original image again. According to the present invention, the images can be compressed based on the low-calculation complexity and objective image quality evaluation, and the visual perception experiences of the users to the images can be guaranteed while an image compression rate is improved. The present invention also discloses an image compression device.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image compression method and device. Background technique [0002] In the Internet big data application environment, with the continuous development of network visual media technology, multimedia entertainment, information highway, etc. have continuously put forward higher requirements for the storage and transmission of information data, especially digital images with huge data volume Communication brings a severe test to the existing limited bandwidth, so image compression technology has received more and more attention. The purpose of image compression is to represent and transmit the original larger image with as few bytes as possible, and require the restored image to have better quality. The use of image compression can reduce the burden of image storage and transmission, enabling fast transmission and real-time processing of images on the network. [...

AI Technical Summary

Problems solved by technology

Later, researchers proposed a classic visual quality evaluation method based on image local feature structure similarity (SSIM), a quality evaluation algorithm for block compression algorithm (BBCQ: Block Based Coding Quality), and a quality evaluation method based on feature similarity. Evaluation algorithm (FSIM: feature similarity) and other objective evaluation algorithms, but the above methods have certain problems, such as BBCQ needs too many initialization parameters, SSIM, FSIM calculation complexity is too high

Images