Video and audio coding hard decision quantification method based on context adaptive displacement model

Abstract

The invention discloses a video and audio coding hard decision quantification method based on a context adaptive displacement model, and the method employs the context adaptive quantification displacement model to achieve a context and code rate threshold value adaption quantification algorithm. The model introduces the correlation between coefficients through simulating optimal soft decision quantification characteristics, and a new CABAC context adaptive displacement model is proposed on the basis of the hard decision quantification. The model achieves the statistics of actual saving conditions of the code rate, and obtains an optimal threshold value which can be used for distinguishing quantification results through a Bayes binary discrimination method. The dynamic adjustment of the threshold value and the code rate displacement quantity are achieved through the model. The experiment indicates that the context adaptive displacement model based on the invention is suitable for implementation of the configuration design of a hardware coder, and the performances of the algorithm approach the optimal SDQ quantification. Moreover, compared with a fixed displacement quantity HDQ quantification method, the method obtains the better improvement of the rate distortion.

Description

technical field [0001] The algorithm of the present invention is applicable to the quantizer design of the H.264 video encoder, and also applicable to the quantizer design of the H.265 / HEVC video encoder, specifically a video encoding hard-decision quantization method based on the context adaptive offset model . Background technique [0002] In video compression coding, a series of measures are taken to achieve higher video compression performance. Quantization can effectively reduce the signal value space, so as to achieve better compression effect. Quantization determines the amount of distortion before and after video compression, and also has a great impact on bit rate control. The quantization of different fields of each coefficient is related to the Cabac context probability index. The index values ​​under the same field are different, and the possibility of being quantized to different values ​​is different. According to this possibility, it can be quantified more a...

AI Technical Summary

Problems solved by technology

[0015] Defects in the prior art: this algorithm is a simplification of the SDQ algorithm, but it is mainly aimed at video encoders that are implemented based on software, and fails to eliminate the seriality caused by the Viterbi algorithm branch selection and context arithmetic coding

[0023] Based on the SDQ algorithm, the computational complexity is high, and the code rate consumption of a coefficient candidate quantization value is related to the quantization results of adjacent coefficients in the current and adjacent blocks. The close correlation and seriality between coefficients make the algorithm in Hardware implementation faces challenges

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