Handling method of transformation coefficient and method and module and coder of wavelet transformation coding

Abstract

The invention discloses a handling method of transformation a coefficient and a method, a module and a code of wavelet transformation coding. A transformation domain of wavelet transformation is divided into small pieces so that primary transformation coefficient of each small piece is quantized and therefore the quantizing process can only be performed effectively in each small piece zone and fidelity in the process of image compression is guaranteed. Quantifying step size adopted is determined according to the practical situation of the transformation coefficient of a processed image. According to a self-adaptation quantitative method, the wavelet transformation coding guarantees that the compressed image is prevented from generating an obvious error in vision and balance is achieved among high compression efficiency of the wavelet transformation coding so that practical application performance of the wavelet transform coding is enhanced.

Description

technical field [0001] The present application relates to the field of encoding technology, and in particular to a processing method of transform coefficients, a wavelet transform encoding method, a module and an encoder. Background technique [0002] Wavelet transform has been used for multi-resolution image description for a long time. The idea of ​​wavelet image decomposition is a special case of subband decomposition. Wavelet decomposition is a complete, orthogonal and multi-resolution decomposition. In the space domain, the wavelet decomposition decomposes the signal into different levels, and at the same time of the decomposition operation, a multi-level decomposition in the frequency domain is formed. At each level in the frequency domain, the distribution of high-frequency components and low-frequency components is related to the direction of the frequency distribution in the original data. The principle of using wavelet transform to compress images is the same as ...

AI Technical Summary

Problems solved by technology

For quantization of transform coefficients, the quantization step size is generally a preset value. The disadvantage is that when the preset quantization step size d is too small to meet the actual situation of the obtained transform coefficients, the transformed Among the coefficients, a large number of small transform coefficients become important, which requires a large number of bits to encode. Therefore, in the case of high bit rates, the advantages of wavelet transform encoding are gradually lost.

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