Adaptive Texture Gradient Prediction Method in Bandwidth Compression

Abstract

The invention relates to a method for adaptive texture gradient prediction in bandwidth compression, which includes selecting N sampling methods to sample pixel components in the current MB; selecting M prediction methods to predict the current MB; and calculating the current MB respectively. The prediction residual and SAD; select the sampling mode and prediction mode of the current MB according to the SAD. The present invention calculates the prediction residual and SAD of the current prediction macroblock by defining the sampling mode of the MB. Compared with the existing methods, when the texture of the image to be compressed is more complex, for the MB at the texture boundary of the current image, according to the texture gradient principle, it does not depend on the surrounding MB of the current MB, but through the current MB The prediction residual obtained by its own texture characteristics can improve the accuracy of the prediction residual value for complex texture areas, further reduce the theoretical limit entropy, and increase the bandwidth compression rate.

Description

technical field [0001] The invention relates to the technical field of compression, in particular to an adaptive texture gradient prediction method in bandwidth compression. Background technique [0002] As TVs and monitors enter ultra-high-definition (4K) and ultra-high-definition (8K) resolutions, as well as the development and popularization of a new generation of cloud computing and information processing models and platforms with remote desktops as a typical form, the video image The demand for data compression is also moving towards higher resolution and composite images that include images captured by cameras and computer screen images, thus making the data volume of video images very large and requiring more storage space and transmission bandwidth. In this case Therefore, it is particularly necessary to use the bandwidth compression technology in the chip to improve the image storage space and transmission bandwidth. [0003] The original video signal has a huge am...

AI Technical Summary

Problems solved by technology

[0004] In the existing texture correlation prediction method, for the macroblock (Macro block, referred to as MB) at the texture boundary in the image, since the current MB and the surrounding MB are not in the same texture area, the correlation between the current MB and the surrounding MB is relatively low. Poor, that is, the correlation between the current MB and the surrounding MB cannot be used to obtain a smaller prediction residual

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