Image coding prediction method based on local minimum entropy

Abstract

The invention relates to an image coding prediction method based on local minimum entropy. The method is especially suitable for compression processing of a static image. The method is characterized by: carrying out wavelet transformation and wavelet coefficient quantification to the image; selecting a wavelet coefficient which has a strong correlation with a bit to be coded as a prediction coefficient; defining an importance state function, an importance state direction weighting function and an importance state and a function of the prediction coefficient; taking reduction of entropy as a discrimination criteria, establishing a local optimum prediction model so as to classify data of the bit to be coded. By using the prediction model established in the invention, high efficient predictive coding of the wavelet coefficient can be realized. An experiment result shows that under a same compression ratio, objective quality of the recovery image can be effectively raised compared with a static image compression standard JPEG 2000.

Description

technical field [0001] The present invention relates to an encoding prediction method suitable for static image compression, in particular to an image encoding prediction method that uses entropy reduction as a basis to predict and classify bit data, thereby approaching the high-order conditional entropy of wavelet coefficients. Image compression technology field. Background technique [0002] In recent ten years, the image compression algorithm based on wavelet transform has been widely used in static image compression, and is considered to be the best compression performance method at present. Its compression process mainly includes four steps: preprocessing, wavelet transform, quantization, and entropy coding, such as figure 1 shown. Among them, entropy coding encodes the data according to the probability of occurrence of the data, which can remove the statistical redundancy between the data, so as to complete the representation of the original image information with th...

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Problems solved by technology

[0005] (1) The correlation between wavelet coefficients is not fully utilized. When predicting coding, the correlation between wavelet coefficients is mainly reflected in amplitude, direction, and regional correlation. Existing methods only use one or two of them , the use of correlation is not comprehensive and sufficient

[0006] (2) When designing a prediction function or building a prediction model, the directional characteristics of the coefficients (such as EBCOT) or the prediction function of a single coefficient (such as PCAS) are overemphasized, but in fact the current bit to be encoded is related to multiple coefficients around it relationship, so the existing methods fail to exert the comprehensive prediction effect of multiple coefficients

[0007] (3) The establishment of the prediction model mainly relies on experience or statistical data, and does not start from the requirements of subsequent entropy coding, so that the different probability distributions of the bits to be coded are not well distinguished, and the high-order entropy of the information source cannot be effectively approached, affecting image compression efficiency

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