基于紋理特性與視覺關注度的HEVC優(yōu)化研究
發(fā)布時間：2019-05-31 18:00
【摘要】：隨著網(wǎng)絡的發(fā)展與視頻應用的普及,用戶對視頻質(zhì)量的要求越來越高,高質(zhì)量的視頻需要大量數(shù)據(jù)描述畫面細節(jié),導致視頻數(shù)據(jù)量激增。高性能視頻編碼(High Efficiency Video Coding,HEVC)是面向高分辨率視頻的新一代編碼標準,其核心目標是在H.264/AVC High Profile的基礎上,將視頻壓縮效率提高一倍。但壓縮效率提高的同時也帶來了較高的計算復雜度與較長的編碼時間,嚴重影響了HEVC的推廣與應用。在視頻中,物體的紋理通過局部區(qū)域像素的排列與變化來表現(xiàn),通常呈緩慢或周期性變化,具有一定的規(guī)律性。HEVC以編碼單元(Coding Unit,CU)為基本單位對圖像進行編碼,將紋理簡單區(qū)域劃分為低深度級別的大尺寸CU,將紋理復雜區(qū)域劃分為高深度級別的小尺寸CU,對紋理相似的區(qū)域CU深度劃分相近。但CU劃分算法計算復雜度高,成為制約HEVC性能的主要因素之一。所以,在HEVC中考慮視頻的紋理特性可以預測CU劃分深度,降低編碼計算復雜度,有效減少編碼時間。另一方面,眼睛是各種視頻信號的最終受體,視頻質(zhì)量也可以說是人眼對視頻感知的主觀質(zhì)量。人類視覺系統(tǒng)并非平等地關注視頻中所有區(qū)域,在視頻編碼中根據(jù)視覺對圖像區(qū)域關注度的不同調(diào)整碼率資源分配,可有效去除視覺冗余,提升壓縮性能。因此,基于紋理特性與視覺關注度的HEVC優(yōu)化研究能夠有效提高HEVC編碼性能,具有重要的理論意義和廣闊的應用價值。首先在深入研究CU劃分原理的基礎上,提出一種基于Canny算子的CU快速劃分算法,使CU提前進入子劃分,降低編碼復雜度,加快編碼過程。然后根據(jù)人眼感知特性建立視覺關注度模型,計算當前最大編碼單元(Largest Coding Unit,LCU)關注度,調(diào)節(jié)不同關注度區(qū)域的碼率資源分配,實現(xiàn)自適應編碼壓縮,提高整體壓縮比。本文的主要研究內(nèi)容包括以下三個方面:(1)研究CU初始深度預測算法,優(yōu)化CU劃分。首先研究CU劃分深度與其鄰域及參考幀相同位置CU深度的相關性,推導出CU初始深度與紋理分布的數(shù)學關系;然后利用Canny分割算子邊緣定位精度高、連續(xù)性良好等優(yōu)點,分割關鍵幀的紋理區(qū)域,并判斷紋理在當前CU與鄰域中分布關系;最后根據(jù)紋理分布情況預測CU初始深度,簡化CU劃分遞歸過程,降低編碼復雜度,加快編碼過程。(2)模擬人類視覺系統(tǒng)的選擇性注意機制建立關注度模型。根據(jù)視覺感知特性引入運動性因子、紋理復雜度因子、對比度因子與亮度因子建立視覺關注度模型。為保證編碼效率,采用計算復雜度低、魯棒性強的灰度投影法計算運動性因子,基于亮度分布情況計算紋理復雜度因子,采用像素四近鄰算法計算對比度因子,采用編碼單元四近鄰算法計算亮度因子。(3)根據(jù)CU關注度的不同,調(diào)整碼率資源分配,實現(xiàn)自適應編碼壓縮。根據(jù)人眼更加關注結構性失真而非像素點失真的特點,對高關注度LCU使用構建的結構相似性失真優(yōu)化算法而非傳統(tǒng)的誤差平方和算法,對低關注度LCU利用關注度修正拉格朗日因子,實現(xiàn)對低關注度區(qū)域粗量化,達到提高壓縮比,減少碼率的效果。
[Abstract]:With the development of the network and the popularization of the video application, the demand for the video quality of the user is higher and higher, and the high-quality video needs a large amount of data to describe the detail of the picture, resulting in a sharp increase in the amount of video data. High-performance Video Coding (HEVC) is a new-generation coding standard for high-resolution video. Its core goal is to double the video compression efficiency on the basis of the H.264/ AVC High Profile. But the compression efficiency is improved, higher calculation complexity and long coding time are also brought, and the popularization and application of the HEVC are seriously affected. In video, the texture of an object is represented by the arrangement and variation of the local area pixels, usually in a slow or periodic manner, with a certain regularity. HEVC is used to encode an image by a coding unit (CU), and the texture simple area is divided into a large-size CU with a low depth level, and the texture complex area is divided into a small-size CU with a high depth level, and the depth of the area CU with similar texture is similar. However, the calculation complexity of the CU is high and becomes one of the main factors that restrict the performance of the HEVC. Therefore, considering the texture characteristics of the video in the HEVC, the division depth of the CU can be predicted, the coding calculation complexity is reduced, and the coding time is effectively reduced. On the other hand, the eye is the final receptor of various video signals, and the video quality can also be said to be the subjective quality of the human eye's perception of the video. The human vision system is not equally concerned with all the areas in the video, and can effectively remove the visual redundancy and improve the compression performance according to the different adjustment code rate resource allocation of the attention of the visual on the image area in the video coding. Therefore, the HEVC optimization research based on the texture characteristic and the visual attention can effectively improve the HEVC coding performance, and has important theoretical significance and wide application value. First, on the basis of in-depth study of the principle of CU division, a fast algorithm of CU based on Canny operator is proposed, which makes the CU enter the sub-division in advance, reduce the coding complexity and speed up the coding process. Then the visual attention model is established according to the perception characteristic of the human eye, the attention of the current maximum coding unit (LCU) is calculated, the code rate resource allocation of the different attention regions is adjusted, the adaptive coding compression is realized, and the overall compression ratio is improved. The main research contents of this paper include the following three aspects: (1) study the initial depth prediction algorithm of the CU, and optimize the CU division. Firstly, the relationship between the depth of the CU division depth and its neighborhood and the same position of the reference frame is studied, the mathematical relation between the initial depth of the CU and the texture distribution is derived, and then the texture region of the key frame is divided by using the advantages of high edge positioning accuracy and good continuity of the Canny division operator. And finally, the initial depth of the CU is predicted according to the texture distribution condition, a recursive process of the CU is simplified, the coding complexity is reduced, and the coding process is accelerated. And (2) simulating the selective attention mechanism of the human vision system to establish the attention model. According to the visual perception characteristics, a visual attention model is established by introducing a motility factor, a texture complexity factor, a contrast factor and a brightness factor. In order to guarantee the coding efficiency, the motion factor is calculated by the gray-scale projection method with low computational complexity and strong robustness, the texture complexity factor is calculated based on the brightness distribution, the contrast factor is calculated by using the four-neighbor algorithm of the pixel, and the brightness factor is calculated by adopting the four-neighbor algorithm of the coding unit. And (3) adjusting the code rate resource allocation according to the different degree of the CU, so as to realize the self-adaptive coding compression. according to the characteristic that the human eye is more concerned with the structural distortion and the non-pixel point distortion, the structure similarity distortion optimization algorithm constructed by the high-degree-of-interest LCU is used instead of the non-traditional error sum-of-square algorithm, and the Lagrange factor is corrected for the low-degree-of-attention LCU by the degree of attention, The coarse quantization of the low-degree-of-attention area is realized, and the effect of improving the compression ratio and reducing the code rate is achieved.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN919.81
本文編號：2489912
[Abstract]:With the development of the network and the popularization of the video application, the demand for the video quality of the user is higher and higher, and the high-quality video needs a large amount of data to describe the detail of the picture, resulting in a sharp increase in the amount of video data. High-performance Video Coding (HEVC) is a new-generation coding standard for high-resolution video. Its core goal is to double the video compression efficiency on the basis of the H.264/ AVC High Profile. But the compression efficiency is improved, higher calculation complexity and long coding time are also brought, and the popularization and application of the HEVC are seriously affected. In video, the texture of an object is represented by the arrangement and variation of the local area pixels, usually in a slow or periodic manner, with a certain regularity. HEVC is used to encode an image by a coding unit (CU), and the texture simple area is divided into a large-size CU with a low depth level, and the texture complex area is divided into a small-size CU with a high depth level, and the depth of the area CU with similar texture is similar. However, the calculation complexity of the CU is high and becomes one of the main factors that restrict the performance of the HEVC. Therefore, considering the texture characteristics of the video in the HEVC, the division depth of the CU can be predicted, the coding calculation complexity is reduced, and the coding time is effectively reduced. On the other hand, the eye is the final receptor of various video signals, and the video quality can also be said to be the subjective quality of the human eye's perception of the video. The human vision system is not equally concerned with all the areas in the video, and can effectively remove the visual redundancy and improve the compression performance according to the different adjustment code rate resource allocation of the attention of the visual on the image area in the video coding. Therefore, the HEVC optimization research based on the texture characteristic and the visual attention can effectively improve the HEVC coding performance, and has important theoretical significance and wide application value. First, on the basis of in-depth study of the principle of CU division, a fast algorithm of CU based on Canny operator is proposed, which makes the CU enter the sub-division in advance, reduce the coding complexity and speed up the coding process. Then the visual attention model is established according to the perception characteristic of the human eye, the attention of the current maximum coding unit (LCU) is calculated, the code rate resource allocation of the different attention regions is adjusted, the adaptive coding compression is realized, and the overall compression ratio is improved. The main research contents of this paper include the following three aspects: (1) study the initial depth prediction algorithm of the CU, and optimize the CU division. Firstly, the relationship between the depth of the CU division depth and its neighborhood and the same position of the reference frame is studied, the mathematical relation between the initial depth of the CU and the texture distribution is derived, and then the texture region of the key frame is divided by using the advantages of high edge positioning accuracy and good continuity of the Canny division operator. And finally, the initial depth of the CU is predicted according to the texture distribution condition, a recursive process of the CU is simplified, the coding complexity is reduced, and the coding process is accelerated. And (2) simulating the selective attention mechanism of the human vision system to establish the attention model. According to the visual perception characteristics, a visual attention model is established by introducing a motility factor, a texture complexity factor, a contrast factor and a brightness factor. In order to guarantee the coding efficiency, the motion factor is calculated by the gray-scale projection method with low computational complexity and strong robustness, the texture complexity factor is calculated based on the brightness distribution, the contrast factor is calculated by using the four-neighbor algorithm of the pixel, and the brightness factor is calculated by adopting the four-neighbor algorithm of the coding unit. And (3) adjusting the code rate resource allocation according to the different degree of the CU, so as to realize the self-adaptive coding compression. according to the characteristic that the human eye is more concerned with the structural distortion and the non-pixel point distortion, the structure similarity distortion optimization algorithm constructed by the high-degree-of-interest LCU is used instead of the non-traditional error sum-of-square algorithm, and the Lagrange factor is corrected for the low-degree-of-attention LCU by the degree of attention, The coarse quantization of the low-degree-of-attention area is realized, and the effect of improving the compression ratio and reducing the code rate is achieved.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN919.81
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