First-order difference prefix notation coding method for lossless compression of image data

Abstract

The invention discloses a first-order difference prefix notation coding method for the lossless compression of image data, which comprises the following steps of: 1, pre-processing original image data to obtain first-order differences between adjacent pixel values; 2, counting the probabilities of occurrence of each first-order difference, and re-sequencing the first-order differences from higher probabilities to lower probabilities to obtain new first-order differences; 3, performing first-order difference prefix notation coding processing on the new first-order differences obtained by the step 2 to obtain a prefix code table and a suffix code table; 4, performing order-reduction processing on suffix codes in the step 3 by adopting a suffix derivation method; and 5, performing coding processing on primary prefix codes in the step 4 according to bit planes by adopting dichotomy. The method ensures relatively lower computational complexity, simple coding, high coding and decoding efficiency remarkably higher than that of JPEG2000, and no need of adopting special compression and decompression chips, and compared with a conventional lossless compression algorithm, remarkably increases a compression ratio to be equivalent to that of the international standard JPEG2000.

Description

technical field [0001] The invention relates to a coding method for lossless compression of image data represented by a first-order difference prefix, and belongs to the technical field of digital image lossless compression. Background technique [0002] In recent years, with the development of multimedia technology and various digital image communication technologies, people have put forward higher requirements on the quality, size and application of digital images, hoping to use limited space and bandwidth resources under the premise of ensuring quality Store and deliver large images. However, the space occupied by digitized images is huge, and relying solely on improving the performance of computer hardware and communication facilities to solve problems cannot keep up with application requirements. Therefore, in order to alleviate the requirements of digital images on storage media and improve the working efficiency of computers, it is very necessary to compress digital ...

AI Technical Summary

Problems solved by technology

However, with the in-depth research and application of these traditional coding methods, it is found that the compression ratio of these methods is low and the compression efficiency is not high.

In order to overcome the shortcomings of the above-mentioned compression coding methods, several new coding methods have been proposed: compression methods based on wavelet transform, fractal compression coding, etc., but these compression coding algorithms are complex, the amount of calculation is large, and special compression and decompression are required. chip, expensive

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