Real-time lossless compression method for image

Abstract

The invention relates to a real-time lossless compression method for an image. The method comprises the following steps: setting the length of a predictor according to the specification of an input initial image; predicting by using a JPEG-LS (Information T) predictor according to the input image, and carrying out subtraction on the actual value and the predicted value to obtain a residual which can be mapped to a nonnegative integer and a preprocessing result of the input image; dividing and accumulating according to the different values of division mode k and the preprocessing result of the input image, and calculating a total code length when k is equal to 0, 1, 2,..., n-3, marking a k value which corresponds to the minimum value of the code length as a k<*>, wherein n is a bit wide when the pixel value of the imported image is converted to a binary system; dividing a sample of the whole image according to the k<*> to obtain a compressed code steam for storing or transmitting. For a gray image with poor continuous color tonality, particularly a spaceborne remote sensing image, the invention can effectively reduce the space redundancy of the whole image and decrease the average value of the sample; and meanwhile, any information cannot be lost during the whole coding process and a lossless compression can be realized.

Description

technical field [0001] The invention relates to the technical field of arbitrary grayscale image information processing, in particular to a real-time lossless compression method that can be applied to common grayscale image processing, especially remote sensing satellites, medical images, and various target images. Background technique [0002] At present, satellite remote sensing technology is widely used in many fields such as military reconnaissance, national and regional security, meteorological analysis, marine and land resource exploration, and various disaster investigations. The amount of various image data is getting larger and larger, resulting in higher and higher requirements for massive data compression. [0003] The existing image compression methods are mainly divided into lossy, lossless and near lossless compression. As the name implies, both lossy and near-lossless compression methods essentially determine that the image decompression and reconstruction re...

AI Technical Summary

Problems solved by technology

[0035] For the multi-parameter, multi-dimensional discrete optimization problem of equation (7), where the number of parameters varies in a large range, there is still no suitable solution method, and for real-time image compression systems, repeated cyclic solutions obviously cannot meet real-time performance. Require

[0036] However, according to the above analysis, since the s bits identified by the splitting method must be added to each group of data, although each group of coding objects has achieved the best compression effect, the output code length of each group of data is at the optimum S bits are added on the basis of the code length, then for the entire image, the cumulative increase bits

and the increased The bit-long data has not attracted much attention, and the increase will be taken into account in the calculation of the actual output code length; but this Bits are indeed the most essential part of the actual output code stream, otherwise the output compressed data cannot be decoded and restored

Images