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Research On Rate-distortion Optimized Quantization Algorithm For HEVC Video Coding

Posted on:2019-04-23Degree:MasterType:Thesis
Country:ChinaCandidate:R Y LiFull Text:PDF
GTID:2428330551460008Subject:Information and Communication Engineering
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With the rapid development of video coding technology,the latest generation of video coding standard HEVC came into being.Owing to the new coding tools and its own core features,HEVC has greatly improved its coding performance.Due to the new coding tools and its own special core technologies,the coding performance of HEVC has been greatly improved.For the quantification algorithm module,it is the root cause of the distortion in video coding,and affects the quality and bit rate of the video at the same time.Generally speaking,the quantizers in video coding are all centered around the zero point,which is often called the "Dead-zone".The dead-zone-based Hard Decision Quantization(HDQ)algorithm is most commonly used,HDQ is based on a memoryless source hypothesis and believes that the quantified results are only related to the quantization step size and signal strength.In contrast,Soft Decision Quantization(SDQ)based on rate-distortion optimization technology takes the influence of rate-distortion coding cost and coefficients into consideration,and achieves coefficient-level quantization control by Viterbi algorithm or path dynamic programming method.But the extremely high complexity leads to SDQ facing great challenges in hardware implementation.In video coding,the DCT coefficient distribution model is the basis of the rate distortion optimization theoretical model,which plays a key role in the control of the coding rate.The tailing phenomenon often occurs in the distribution of the actual sample coefficients,and it cannot be accurately fitted with the mainstream exponential decay function.This paper aims to optimize dead-zone hard decision quantification algorithms.A segmented truncated DCT distribution model——TCM model is used in this paper.The study found that the probability that the actual sample falls into the main body and the tail has a great influence on the model distribution parameters of the coefficients of different frequency components in the block.Therefore,the probability factors and distribution parameters in the model are used as important modeling parameters for the adaptive dead-zone offset.An adaptive dead-time offset model based on the TCM model is constructed offline.Compared with the HDQ with a fixed offset,the peak signal-to-noise ratio of the proposed algorithm has a performance improvement of 0.08017 dB,an average saving rate of 2.9411%,and a better approximation to the rate-distortion performance of SDQ.Based on the adaptive hard-decision quantization algorithm,this paper further considers the characteristics of HVS sensing.The human eye is insensitive to high frequency details in the image,so different quantization matrix coefficients are used for the difference between high and low frequency components in images and videos.According to the Bayesian minimum error probability constraint and the minimum misjudgment probability,by simulating the mechanism of the SDQ algorithm,in the transform block,quantization step sizes are adopted for the high and low frequency components in different positions and different Qp,and a perceptual quantization matrix model based on content adaptation is constructed.What's more,the subjective quality of the image is evaluated by a structural similarity method.The experimental results show that compared with the traditional HDQ algorithm,the algorithm can achieve an average rate reduction of 5.048%,especially WVGA and WQVGA formats achieve an average rate savings of 10.65%.The subjective quality of the image has been improved a lot.
Keywords/Search Tags:video coding, soft-decision quantization, rate-distortion optimization, hard-decision quantization, quantization matrix
PDF Full Text Request
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