Image lossless compression method

Abstract

The embodiment of the invention provides an image lossless compression method. The method comprises the following steps: acquiring an image to be coded; sequentially filling the to-be-coded image with the basic shapes according to the weights of the basic shapes from large to small; according to a codebook obtained by training, obtaining a codeword corresponding to a basic shape used for filling the to-be-coded image; sequentially storing the position and the code word of the basic shape used for each filling as a triple, wherein the triple comprises the row position, the column position and the code word of the basic shape; and combining the triple set with the head information of the to-be-coded image to generate a compressed file of the to-be-coded image. According to the method provided by the embodiment of the invention, the image is represented by using the code word corresponding to the basic shape in the image and the position of the shape in the image, meanwhile, coding redundancy and space redundancy are efficiently eliminated, and image coding and decoding with a high compression ratio can be realized on the premise of not losing image quality.

Description

technical field [0001] The invention relates to the field of information technology, in particular to an image lossless compression method. Background technique [0002] With the development of the Internet and the advent of the era of big data, images are getting closer and closer to human daily life. Pictures on social software, CT used in medicine, and topographic maps obtained by remote sensing are all stored and displayed in the form of images, because the information carried by images can be easily captured and accepted by human vision. The storage and transmission of massive image data requires a large amount of storage space and communication bandwidth, and image compression can reduce the space required to represent images while ensuring image quality, thereby facilitating image storage and transmission. [0003] Image compression techniques are divided into lossy compression and lossless compression. Among them, lossy compression is to remove some non-critical da...

AI Technical Summary

Problems solved by technology

Among them, lossy compression is to remove some non-critical data information on the premise of ensuring image fidelity to reduce the space required for storing images. The compression ratio can reach a high level, thereby reducing image data during storage and transmission. consumes resources, but cannot fully restore the original data of the image

The lossless compression is to optimize the data storage method and use the statistical redundancy of the data to compress, so that the original data can be completely restored without causing any distortion. However, due to the theoretical limitation of the statistical redundancy of the data, the compression ratio of the lossless compression is much lower. lossy compression

[0004]The existing image compression technology is facing the dilemma that the image compression ratio and image fidelity cannot be balanced

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