Improved spiht image encoding and decoding method with edge enhancement

Abstract

The present invention discloses an edge enhancement improved SPIHT image coding and decoding method. According to the method, the high-pass filtering is carried out on an image low frequency sub-band after wavelet transformation to extract the main contour and the edges of an image, so that on one hand, the synchronization between a coding terminal and a decoding terminal is realized by utilizing the lowest frequency sub-band, the important parameters can be positioned without needing to transmit the synchronization information additionally, and the synchronization information content of the conventional SPIHT coding is reduced; on the other hand, the priority degree of the coding and decoding is controlled according to a high pass filtering result of the low frequency sub-band, and the priority decoding is carried out on the main contour and the edges which are most sensitive to a human visual system in the image, and accordingly, the subjective and objective quality of the decoded images is improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an improved SPIHT image encoding and decoding method with less synchronous information and high subjective and objective quality edge enhancement. Background technique [0002] With the continuous development of network multimedia technology and its applications, people's requirements for image coding continue to increase, which not only requires high reconstruction quality of decoded images, but also requires it to be able to adapt to application requirements such as network heterogeneity and terminal computing capabilities. . This requires a good solution to remove all kinds of redundancy under the premise of ensuring image quality, use as few bits as possible to represent image information, and at the same time make the image encoding stream well adapt to fluctuations in network bandwidth And the decoding requirements of terminals with different computing capabilities. Scalab...

AI Technical Summary

Problems solved by technology

However, on the one hand, these methods spend a considerable amount of bit overhead on the synchronization information, which affects the quality of the reconstructed image, especially at low bit rates.

On the other hand, existing methods treat all transformation coefficients equally, and research on the structural similarity SSIM (Structural Similarity) index shows that the human visual system is more sensitive to structural differences in images than brightness differences. Specifically, The human visual system is more sensitive to the pixel values ​​of edge and contour areas than to the pixel values ​​of flat areas, so treating all coefficients equally is not conducive to ensuring the subjective quality of decoded images

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