本文關(guān)鍵詞： 3D視頻編碼 深度信息 ROI JND H.264 量化參數(shù)　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：3D視頻在帶給人身臨其境的視覺體驗(yàn)的同時(shí),也因其龐大的數(shù)據(jù)量而給視頻的存儲(chǔ)和傳輸帶來了巨大的壓力。因此,如何在保證視頻主觀質(zhì)量的前提下,盡可能占用更小的傳輸帶寬是3D視頻編碼領(lǐng)域亟待解決的重大挑戰(zhàn)。以往的編碼算法主要集中精力于去除視頻的空間冗余、時(shí)間冗余等,而我們將注意力轉(zhuǎn)向于去除視頻中的視覺冗余。為此,本文提出把人眼視覺特性與現(xiàn)有的H.264編碼框架相結(jié)合,著力于利用3D視頻中的感興趣區(qū)域和恰可察覺失真,在保證主觀質(zhì)量的前提下,盡可能減少碼率,最終提高壓縮效率。感興趣區(qū)域(Region Of Interest,ROI)編碼是指通過控制視頻背景區(qū)域與ROI區(qū)域的宏塊量化參數(shù)(Quantization Parameter,QP)的分配,在保證視頻主觀質(zhì)量的同時(shí)提高壓縮效率。對(duì)2D視頻而言,ROI檢測(cè)性能不穩(wěn)定,這限制了 ROI編碼的推廣與使用。而3D視頻中包含的深度信息與人類視覺模型(HVS)中感興趣程度間有著十分密切的聯(lián)系,這為3D視頻ROI區(qū)域檢測(cè)提供了有利條件。因此本文綜合深度信息,提出了兩種3D顯著性檢測(cè)算法。恰可察覺失真(Just Noticeable Distortion,JND)是指由人類視覺系統(tǒng)的生理特性和心理特性所造成的,對(duì)圖像不同區(qū)域具有不同失真敏感度的現(xiàn)象。當(dāng)圖像特定區(qū)域的失真程度低于JND閾值時(shí),人眼無法感知其存在。JND視頻編碼技術(shù)主要針對(duì)視頻的視覺冗余,在編碼時(shí)結(jié)合人眼視覺特性,合理分配編碼資源,進(jìn)一步提高編碼的效率。本文主要利用3D視頻(單視點(diǎn)視頻加深度圖格式)中的紋理和深度信息,對(duì)其進(jìn)行與H.264標(biāo)準(zhǔn)兼容的ROI和JND編碼的研究。本文首先研究了人眼立體視覺系統(tǒng)模型,分析了景物深度與HVS中感興趣程度間的關(guān)系。由此,提出了基于深度的立體投影顯著性檢測(cè)算法;進(jìn)一步發(fā)掘深度和場(chǎng)景中背景區(qū)域的關(guān)系,提出了基于背景檢測(cè)的3D顯著性檢測(cè)算法。針對(duì)人眼對(duì)不同深度的注意程度不同,以及不同視點(diǎn)間物體的相互遮蓋等關(guān)系,提出了一種利用深度圖計(jì)算JND閾值的模型。之后,探討了 H.264壓縮標(biāo)準(zhǔn)和壓縮后比特率的構(gòu)成和調(diào)整方式。據(jù)此,在視頻進(jìn)行H.264壓縮前,通過結(jié)合ROI和JND對(duì)視頻幀的區(qū)域劃分,建立更加符合人眼視覺特性的分級(jí)量化模型,指導(dǎo)人眼感興趣區(qū)域量化參數(shù)的選取,進(jìn)一步提升ROI區(qū)域的主觀質(zhì)量并提高編碼效率。最后,從理論和實(shí)驗(yàn)兩方面,分析了這個(gè)分級(jí)量化策略對(duì)視頻壓縮后比特率的影響。實(shí)驗(yàn)結(jié)果表明本文的方案在同等碼率下,以較低失真保存了人眼視覺敏感區(qū)域,為用戶提供了較好的視覺體驗(yàn)。
[Abstract]:3D video not only brings people the experience of visual experience, but also brings great pressure to the storage and transmission of video because of its huge amount of data. Therefore, how to ensure the subjective quality of video. Using as little transmission bandwidth as possible is a major challenge to be solved in 3D video coding field. Previous coding algorithms mainly focus on removing spatial redundancy and time redundancy of video. We turn our attention to removing visual redundancy in video. Therefore, this paper proposes to combine human visual characteristics with the existing H.264 coding framework. We focus on making use of the region of interest and detectable distortion in 3D video to reduce the bit rate as much as possible on the premise of ensuring subjective quality. Finally, the compression efficiency is improved. The region of interest (ROI) is region of Interest. ROI) coding refers to the allocation of quantization parameters of macroblock quantization by controlling the video background area and the ROI region. The subjective quality of video is guaranteed and the compression efficiency is improved. For 2D video, the performance of ROI detection is unstable. This limits the promotion and use of ROI coding, and there is a close relationship between the depth information contained in 3D video and the degree of interest in the human visual model. This provides a favorable condition for 3D video ROI region detection, so this paper synthesizes depth information. Two 3D salience detection algorithms are proposed, which can detect the distortion and just Noticeable Distortion. JND) is a phenomenon caused by the physiological and psychological characteristics of human visual system and has different distortion sensitivity to different regions of the image. When the distortion degree of a particular region of the image is lower than the threshold of JND. The human eye can not perceive its existence. JND video coding technology is mainly aimed at the visual redundancy of video. When coding, combining with the visual characteristics of human eyes, reasonable allocation of coding resources. Further improve the efficiency of coding. This paper mainly uses the texture and depth information in 3D video (single view video plus depth map format). The ROI and JND codes which are compatible with H.264 standard are studied. Firstly, the model of human stereoscopic vision system is studied in this paper. The relationship between the depth of scene and the degree of interest in HVS is analyzed. Therefore, an algorithm of stereoscopic projection salience detection based on depth is proposed. Further explore the relationship between depth and background area in the scene, a 3D salience detection algorithm based on background detection is proposed. A model for calculating JND threshold by depth map is proposed. The structure and adjustment of H.264 compression standard and compressed bit rate are discussed. According to this, the video frame is divided by combining ROI and JND before H.264 compression. Establish a hierarchical quantization model more in line with the human visual characteristics, guide the selection of quantization parameters of the region of interest, further improve the subjective quality of the ROI region and improve the coding efficiency. Finally. The effects of this hierarchical quantization strategy on the bit-rate of video compression are analyzed theoretically and experimentally. The experimental results show that the proposed scheme preserves the human visual sensitive region with low distortion at the same bit rate. For users to provide a better visual experience.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN919.81

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