Lossless encoding and decoding method of Bayer image

Abstract

The invention provides a lossless encoding and decoding method of a Bayer image. The method comprises a lossless encoding process and a lossless decoding process of the Bayer image; the encoding process comprises: selecting the Bayer image, performing block division on a single image, predicting each block, performing entropy encoding on a prediction residual and a prediction mode obtained on the basis of the block, and finally obtaining a code stream of each block; and the decoding process comprises: performing entropy decoding on the code stream of each block to obtain the prediction residual of each pixel, performing reverse prediction according to the prediction mode used in the encoding process, and reconstructing the pixel value of each block to obtain the original Bayer image. By adoption of the method, the memory cost of a front end processing chip in a camera is saved, the design and manufacturing costs of the chip are also reduced, and the compression time is shortened while a high compression ratio is guaranteed. The experimental results show that the compression ratio of the method can reach 1:1.9 on average, and the complexity is low.

Description

technical field [0001] The invention relates to a method for image compression and decompression, belonging to the technical field of image processing. Background technique [0002] In recent years, digital cameras have been used more and more widely, involving fields such as medicine, scientific research, consumption, national defense, and commerce. Due to the development of camera sensors, the resolution is getting higher and higher, and the requirements for storage space and transmission bandwidth are also gradually increasing. Therefore, the compression coding of video images is becoming more and more important. [0003] Image compression coding is to transform and combine the image information sources to be processed with certain rules, reduce the redundancy and correlation between image information, and finally use as few symbols as possible to represent as much image information as possible. Image compression coding can be divided into lossy compression coding and l...

AI Technical Summary

Problems solved by technology

[0005] The compression rate of the above-mentioned existing image compression technology is low, the memory overhead of the front-end processing chip in the camera is relatively large, and the design and manufacturing costs of the chip are relatively high

Images