Conversion compression based maximum error image compression method

Abstract

Disclosed is a conversion compression based maximum error image compression method. The method comprises performing first-stage row conversion compression and storing generated approximate values (mean value) and detail components (conversion coefficients); performing first-stage column conversion compression on the newly generated approximate value portion, and storing the corresponding approximate values and detail components; performing next-stage row and column conversion compression on the newly generated approximate values till only one next approximate value is left to obtain a final compression matrix; performing data reconstruction through the approximate value and the detail components. According to the method, the image compression method based on the maximum error criterion is used, so that errors of all points after data reconstruction are within the given range, and subsequent inquiry is facilitated; meanwhile, compared with a maximum error criterion based image compression method, the method has the advantages that compression time is shortened, and the compression ratio and the reconstruction quality of images are improved.

Description

technical field [0001] The invention relates to a conversion and compression method of image data and a data reconstruction method, belonging to the technical field of electric communication. Background technique [0002] With the development of information technology, image information is widely used in multimedia communication and computer systems. The remarkable feature of image data is the large amount of information. The huge amount of information obviously brings the problems of "not being able to store", "not being able to check quickly", and "not being able to calculate accurately" to the data processing. Although the channel transmission bandwidth is continuously widening, and the storage capacity of disks and hard disks is increasing, it still cannot solve the fundamental problem of huge data volume, and data compression technology can save data storage space and improve the efficiency of data transmission. [0003] For images, lossy compression techniques are ge...

AI Technical Summary

Problems solved by technology

This method uses already processed data to predict the current pixel value, which will degrade the prediction performance, resulting in a decrease in the quality of image reconstruction.

Some scholars use the method of wavelet transform to convert the error limit of the pixel domain into the limit of the coefficient in the wavelet outline, and use the dynamic programming strategy to determine the selection of the coefficient threshold. However, the time complexity of the algorithm is relatively high, which is O (N 2 BlogB), where N represents the original data size, and B represents the size of the wavelet outline

If some scholars believe that the selection of coefficients in wavelet outlines in the past is selected from wavelet decomposition coefficients, this is not necessary and cannot guarantee the best quality wavelet outline, so they proposed a non-restricted wavelet outline technology, which The algorithmic complexity of the technique is polynomial complexity

Recently, some scholars have proposed a conversion compression method. The algorithm is also an unrestricted wavelet outline technology. The time complexity of the algorithm is further reduced to O(N), but the algorithm is not enough in the application of image compression. Effective, its compression time, image reconstruction quality and compression ratio need to be further improved

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