隨著立體圖像和視頻技術(shù)的進(jìn)步以及國(guó)際三維成像標(biāo)準(zhǔn)的不斷提高,人類正在開(kāi)啟一個(gè)3D視覺(jué)應(yīng)用的新時(shí)代。圖像處理技術(shù)的發(fā)展和顯示設(shè)備的多樣化迫切需要在各種設(shè)備上顯示立體內(nèi)容。為了適應(yīng)不同的觀看需求,需要調(diào)整圖像的大小,以便在不同的終端設(shè)備上實(shí)現(xiàn)良好的顯示效果。最簡(jiǎn)單的圖像大小調(diào)整方法是均勻縮放,但這種方法往往會(huì)導(dǎo)致圖像中重要對(duì)象的失真,進(jìn)而影響觀看舒適性。與傳統(tǒng)的二維視覺(jué)內(nèi)容相比,立體視覺(jué)引入了額外的深度維度,為用戶帶來(lái)了更多的享受。然而,這一額外的維度也會(huì)給立體視覺(jué)成像質(zhì)量帶來(lái)一些額外的挑戰(zhàn)和制約。因此,有必要在盡可能避免視覺(jué)畸變、保持深度、獲得最佳圖像質(zhì)量感知的前提下實(shí)現(xiàn)圖像尺寸的調(diào)整,這個(gè)過(guò)程被稱為立體圖像重定向。然而,由于目前的立體圖像重定向技術(shù)還不夠成熟,立體圖像重定向算子對(duì)圖像質(zhì)量、深度感知和視覺(jué)舒適度的影響帶來(lái)了不同的扭曲和失真。失真的立體圖像會(huì)嚴(yán)重影響用戶的視覺(jué)體驗(yàn),甚至?xí)斐梢恍┮曈X(jué)健康問(wèn)題。因此,重定向立體圖像的質(zhì)量評(píng)價(jià)在標(biāo)立體圖像重定向的應(yīng)用和用戶體驗(yàn)質(zhì)量方面發(fā)揮著至關(guān)重要的作用。探討如何建立與人眼主觀感知相一致的客觀立體圖像重定向質(zhì)量評(píng)價(jià)模型,是立體圖像處理領(lǐng)域的研究...

【文章頁(yè)數(shù)】：100 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
ABSTRACT
Chapter 1 Introduction
    1.1 Motivation
    1.2 Objectives
    1.3 Research Contributions
    1.4 Thesis Structure
Chapter 2 Background and Related Work
    2.1 Stereoscopic Image Retargeting Techniques
        2.1.1 Stereoscopic Image Retargeting Problem
        2.1.2 Stereoscopic Image Retargeting Operators
        2.1.3 Stereoscopic Image Retargeting Distortion Analysis
        2.1.4 Discussion
    2.2 Image Quality Assessment
        2.2.1 Image Quality Overview
        2.2.2 2D Image Quality Assessment Methods
        2.2.3 3D Image Quality Assessment
        2.2.4 Image Retargeting Quality Assessment
    2.3 Summery
Chapter 3 Subjective Stereoscopic Image Retargeting Quality As-sessment
    3.1 Image Acquisition and Selection
    3.2 Test Stimuli
    3.3 Subjective Testing
    3.4 Analysis of Results
        3.4.1 Measuring the effect of retargeting methods on image quality degradation
        3.4.2 Measuring the effect of scene content on image quality degradation
        3.4.3 The correlation between the different quality aspects and overall quality
    3.5 Summery
Chapter 4 Triangulation-based Objective Quality AssessmentMethod for Stereoscopic Image Retargeting
    4.1 Feature Extraction
        4.1.1 Image quality
        4.1.2 Visual comfort
        4.1.3 Depth perception
    4.2 Feature Fusion
    4.3 Experimental Results
        4.3.1 Database
        4.3.2 Performance comparison with other methods
        4.3.3 Performance of each quality component
        4.3.4 Discussion
    4.4 Summery
Chapter 5 Saliency-based Seam Carving Using Adaptive Segmen-tation
    5.1 Overview
        5.1.1 Energy Function
        5.1.2 Saliency Map
    5.2 Proposed Method
        5.2.1 Adaptive Segmentation
        5.2.2 Saliency-based Seam Carving
        5.2.3 Stereoscopic Case
    5.3 Experimental Results
        5.3.1 Image Retargeting Dataset
        5.3.2 Comparison Experiment
        5.3.3 Ablation Experiment
        5.3.4 Discussion
    5.4 Summery
Chapter 6 Conclusion and Future Work
    6.1 Conclusion
    6.2 Future Work
Bibliography
Acknowledgements
Publications



本文編號(hào)：3982690