HEVC based Mixed-Resolution Stereo Video Codec

This paper presents a High Efficiency Video Codec (HEVC) based spatial mixed-resolution stereo video codec. The proposed codec applies a frame interleaving algorithm to reorder the stereo video frames into a monoscopic video. The challenge for mixed-resolution video coding is to enable the codec to encode frames with different frame resolutions. This issue is addressed by superimposing a low resolution replica of the decoded I-frame on its respective decoded picture, where remaining space of the frame is set to zero. This significantly reduces the computation cost for finding the best match. The proposed codec’s reference frames structure is designed to efficiently exploit both temporal and inter-view correlations. Performance of the proposed codec is assessed using five standard multiview video datasets and benchmarked against that of the anchor and the state-of-the-art techniques. Results show that the proposed codec yields significantly higher coding performance compared to the anchor and state-of-the-art techniques

Compression and Subjective Quality Assessment of 3D Video

In recent years, three-dimensional television (3D TV) has been broadly considered as the successor to the existing traditional two-dimensional television (2D TV) sets. With its capability of offering a dynamic and immersive experience, 3D video (3DV) is expected to expand conventional video in several applications in the near future. However, 3D content requires more than a single view to deliver the depth sensation to the viewers and this, inevitably, increases the bitrate compared to the corresponding 2D content. This need drives the research trend in video compression field towards more advanced and more efficient algorithms. Currently, the Advanced Video Coding (H.264/AVC) is the state-of-the-art video coding standard which has been developed by the Joint Video Team of ISO/IEC MPEG and ITU-T VCEG. This codec has been widely adopted in various applications and products such as TV broadcasting, video conferencing, mobile TV, and blue-ray disc. One important extension of H.264/AVC, namely Multiview Video Coding (MVC) was an attempt to multiple view compression by taking into consideration the inter-view dependency between different views of the same scene. This codec H.264/AVC with its MVC extension (H.264/MVC) can be used for encoding either conventional stereoscopic video, including only two views, or multiview video, including more than two views. In spite of the high performance of H.264/MVC, a typical multiview video sequence requires a huge amount of storage space, which is proportional to the number of offered views. The available views are still limited and the research has been devoted to synthesizing an arbitrary number of views using the multiview video and depth map (MVD). This process is mandatory for auto-stereoscopic displays (ASDs) where many views are required at the viewer side and there is no way to transmit such a relatively huge number of views with currently available broadcasting technology. Therefore, to satisfy the growing hunger for 3D related applications, it is mandatory to further decrease the bitstream by introducing new and more efficient algorithms for compressing multiview video and depth maps. This thesis tackles the 3D content compression targeting different formats i.e. stereoscopic video and depth-enhanced multiview video. Stereoscopic video compression algorithms introduced in this thesis mostly focus on proposing different types of asymmetry between the left and right views. This means reducing the quality of one view compared to the other view aiming to achieve a better subjective quality against the symmetric case (the reference) and under the same bitrate constraint. The proposed algorithms to optimize depth-enhanced multiview video compression include both texture compression schemes as well as depth map coding tools. Some of the introduced coding schemes proposed for this format include asymmetric quality between the views. Knowing that objective metrics are not able to accurately estimate the subjective quality of stereoscopic content, it is suggested to perform subjective quality assessment to evaluate different codecs. Moreover, when the concept of asymmetry is introduced, the Human Visual System (HVS) performs a fusion process which is not completely understood. Therefore, another important aspect of this thesis is conducting several subjective tests and reporting the subjective ratings to evaluate the perceived quality of the proposed coded content against the references. Statistical analysis is carried out in the thesis to assess the validity of the subjective ratings and determine the best performing test cases

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency

Stereoscopic high dynamic range imaging

Two modern technologies show promise to dramatically increase immersion in virtual environments. Stereoscopic imaging captures two images representing the views of both eyes and allows for better depth perception. High dynamic range (HDR) imaging accurately represents real world lighting as opposed to traditional low dynamic range (LDR) imaging. HDR provides a better contrast and more natural looking scenes. The combination of the two technologies in order to gain advantages of both has been, until now, mostly unexplored due to the current limitations in the imaging pipeline. This thesis reviews both fields, proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the challenges that need to be resolved to enable SHDR and focuses on capture and compression aspects of that pipeline. The problems of capturing SHDR images that would potentially require two HDR cameras and introduce ghosting, are mitigated by capturing an HDR and LDR pair and using it to generate SHDR images. A detailed user study compared four different methods of generating SHDR images. Results demonstrated that one of the methods may produce images perceptually indistinguishable from the ground truth. Insights obtained while developing static image operators guided the design of SHDR video techniques. Three methods for generating SHDR video from an HDR-LDR video pair are proposed and compared to the ground truth SHDR videos. Results showed little overall error and identified a method with the least error. Once captured, SHDR content needs to be efficiently compressed. Five SHDR compression methods that are backward compatible are presented. The proposed methods can encode SHDR content to little more than that of a traditional single LDR image (18% larger for one method) and the backward compatibility property encourages early adoption of the format. The work presented in this thesis has introduced and advanced capture and compression methods for the adoption of SHDR imaging. In general, this research paves the way for a novel field of SHDR imaging which should lead to improved and more realistic representation of captured scenes

Asymmetric 3D video coding based on regions of perceptual relevance

This dissertation presents a study and experimental research on asymmetric coding of stereoscopic video. A review on 3D technologies, video formats and coding is rst presented and then particular emphasis is given to asymmetric coding of 3D content and performance evaluation methods, based on subjective measures, of methods using asymmetric coding. The research objective was de ned to be an extension of the current concept of asymmetric coding for stereo video. To achieve this objective the rst step consists in de ning regions in the spatial dimension of auxiliary view with di erent perceptual relevance within the stereo pair, which are identi ed by a binary mask. Then these regions are encoded with better quality (lower quantisation) for the most relevant ones and worse quality (higher quantisation) for the those with lower perceptual relevance. The actual estimation of the relevance of a given region is based on a measure of disparity according to the absolute di erence between views. To allow encoding of a stereo sequence using this method, a reference H.264/MVC encoder (JM) has been modi ed to allow additional con guration parameters and inputs. The nal encoder is still standard compliant. In order to show the viability of the method subjective assessment tests were performed over a wide range of objective qualities of the auxiliary view. The results of these tests allow us to prove 3 main goals. First, it is shown that the proposed method can be more e cient than traditional asymmetric coding when encoding stereo video at higher qualities/rates. The method can also be used to extend the threshold at which uniform asymmetric coding methods start to have an impact on the subjective quality perceived by the observers. Finally the issue of eye dominance is addressed. Results from stereo still images displayed over a short period of time showed it has little or no impact on the proposed method

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Perceptual quality assessment and processing for visual signals.

視覺信號，包括圖像，視頻等，在采集，壓縮，存儲，傳輸，重新生成的過程中都會被各種各樣的噪聲所影響，因此他們的主觀質量也就會降低。所以，主觀視覺質量在現今的視覺信號處理跟通訊系統中起到了很大的作用。這篇畢業論文主要討論質量評價的算法設計，以及這些衡量標準在視覺信號處理上的應用。這篇論文的工作主要包括以下五個方面。第一部分主要集中在具有完全套考原始圖像的圖像質量評價。首先我們研究人類視覺系統的特征。具體說來，視覺在結構化失真上面的水平特性和顯著特征會被建模然后應用到結構相似度(SSIM)這個衡量標準上。實驗顯示我們的方法明顯的提高了衡量標準典主觀評價的相似度。由這個質量衡量標準的啟發，我們設計了一個主觀圖像壓縮的方法。其中我們提出了一個自適應的塊大小的超分辨率算法指導的下采樣的算法。實驗結果證明提出的圖像壓縮算法無論在主觀還是在客觀層面都構建了高質量的圖像。第二個部分的工作主要討論具有完全參考原始視頻的視頻質量評價。考慮到人類視覺系統的特征，比如時空域的對此敏感函數，眼球的移動，紋理的遮掩特性，空間域的一致性，時間域的協調性，不同塊變換的特性，我們設計了一個自適應塊大小的失真閾值的模型。實驗證明，我們提出的失真閾值模型能夠更精確的描迷人類視覺系統的特性。基于這個自適應塊大小的失真閾值模型，我們設計了一個簡單的主觀質量評價標準。在公共的圓像以及視頻的主觀數據庫上的測試結果證明了這個簡單的評價標準的有效性。因此，我們把這個簡單的質量標準應用于視頻編碼系統中。它可以在同樣的碼率下提供更高主觀質量的視頻。第三部分我們討論具有部分參考信息的圖像質量評價。我們通過描迷重組后的離散余弦變換域的系數的統計分布來衡量圖像的主觀質量。提出的評價標準發掘了相鄰的離散余弦系數的相同統計特性，相鄰的重組離散余弦系數的互信息，以及圖像的能量在不同頻率下的分布。實驗結果證明我們提出的質量標準河以超越其他的具有部分參考信息的質量評價標準，甚至還超過了具有完全參考信息的質量評價標準。而且，提取的特征很容易被編碼以及隱藏到圖像中以便于在圖像通訊中進行質量監控。第四部分我們討論具有部分參考信息的視頻質量評價。我們提取的特征可以很好的描迷空間域的信息失，和時間域的相鄰兩幀間的直方圖的統計特性。在視頻主觀質量的數據庫上的實驗結果，也證明了提出的方法河以超越其他代表性的視頻質量評價標準，甚至是具有完全參考信息的質量評價標準， 譬如PSNR以及SSIM 。我們的方法只需要很少的特征來描迷每一幀視頻圖像。對于每一幀圖像，一個特征用于描迷空間域的特點，另外三個特征用于描述時間域的特點。考慮到計算的復雜度以及壓縮特征所需要的碼率，提出的方法河以很簡單的在視頻的傳輸過程中監控視頻的質量。之前的四部分提到的主觀質量評價標準主要集中在傳統的失真上面， 譬如JPEG 圖像壓縮， H.264視頻壓縮。在最后一部分，我們討論在圖像跟視頻的retargeting過程中的失真。現如今，隨著消費者電子的發展，視覺信號需要在不同分辨率的顯示設備上進行通訊交互。因此， retargeting的算法把同一個原始圖像適應于不同的分辨率的顯示設備。這樣的過程就會引入圖像的失真。我們研究了對于retargeting圖像主觀質量的測試者的分數，從三個方面進行討論測試者對于retargeting圖像失真的反應.圖像retargeting的尺度，圖像retargeting的算法，原始圖像的內容特性。通過大量的主觀實驗測試，我們構建了一個關于圖像retargeting的主觀數據庫。基于這個主觀數據庫，我們評價以及分析了幾個具有代表性的質量評價標準。Visual signals, including images, videos, etc., are affected by a wide variety of distortions during acquisition, compression, storage, processing, transmission, and reproduction processes, which result in perceptual quality degradation. As a result, perceptual quality assessment plays a very important role in today's visual signal processing and communication systems. In this thesis, quality assessment algorithms for evaluating the visual signal perceptual quality, as well as the applications on visual signal processing and communications, are investigated. The work consists of five parts as briefly summarized below.The first part focuses on the full-reference (FR) image quality assessment. The properties of the human visual system (HVS) are firstly investigated. Specifically, the visual horizontal effect (HE) and saliency properties over the structural distortions are modelled and incorporated into the structure similarity index (SSIM). Experimental results show significantly improved performance in matching the subjective ratings. Inspired by the developed FR image metric, a perceptual image compression scheme is developed, where the adaptive block-based super-resolution directed down-sampling is proposed. Experimental results demonstrated that the proposed image compression scheme can produce higher quality images in terms of both objective and subjective qualities, compared with the existing methods.The second part concerns the FR video quality assessment. The adaptive block-size transform (ABT) based just-noticeable difference (JND) for visual signals is investigated by considering the HVS characteristics, e.g., spatio-temporal contrast sensitivity function (CSF), eye movement, texture masking, spatial coherence, temporal consistency, properties of different block-size transforms, etc. It is verified that the developed ABT based JND can more accurately depict the HVS property, compared with the state-of-the-art JND models. The ABT based JND is thereby utilized to develop a simple perceptual quality metric for visual signals. Validations on the image and video subjective quality databases proved its effectiveness. As a result, the developed perceptual quality metric is employed for perceptual video coding, which can deliver video sequences of higher perceptual quality at the same bit-rates.The third part discusses the reduced-reference (RR) image quality assessment, which is developed by statistically modelling the coe cient distribution in the reorganized discrete cosine transform (RDCT) domain. The proposed RR metric exploits the identical statistical nature of the adjacent DCT coefficients, the mutual information (MI) relationship between adjacent RDCT coefficients, and the image energy distribution among different frequency components. Experimental results demonstrate that the proposed metric outperforms the representative RR image quality metrics, and even the FR quality metric, i.e., peak signal to noise ratio (PSNR). Furthermore, the extracted RR features can be easily encoded and embedded into the distorted images for quality monitoring during image communications.The fourth part investigates the RR video quality assessment. The RR features are extracted to exploit the spatial information loss and the temporal statistical characteristics of the inter-frame histogram. Evaluations on the video subjective quality databases demonstrate that the proposed method outperforms the representative RR video quality metrics, and even the FR metrics, such as PSNR, SSIM in matching the subjective ratings. Furthermore, only a small number of RR features is required to represent the original video sequence (each frame requires only 1 and 3 parameters to depict the spatial and temporal characteristics, respectively). By considering the computational complexity and the bit-rates for extracting and representing the RR features, the proposed RR quality metric can be utilized for quality monitoring during video transmissions, where the RR features for perceptual quality analysis can be easily embedded into the videos or transmitted through an ancillary data channel.The aforementioned perceptual quality metrics focus on the traditional distortions, such as JPEG image compression noise, H.264 video compression noise, and so on. In the last part, we investigate the distortions introduced during the image and video retargeting process. Nowadays, with the development of the consumer electronics, more and more visual signals have to communicate between different display devices of different resolutions. The retargeting algorithm is employed to adapt a source image of one resolution to be displayed in a device of a different resolution, which may introduce distortions during the retargeting process. We investigate the subjective responses on the perceptual qualities of the retargeted images, and discuss the subjective results from three perspectives, i.e., retargeting scales, retargeting methods, and source image content attributes. An image retargeting subjective quality database is built by performing a large-scale subjective study of image retargeting quality on a collection of retargeted images. Based on the built database, several representative quality metrics for retargeted images are evaluated and discussed.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Ma, Lin."December 2012."Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.Includes bibliographical references (leaves 185-197).Abstract also in Chinese.Dedication --- p.iiAcknowledgments --- p.iiiAbstract --- p.viiiPublications --- p.xiNomenclature --- p.xviiContents --- p.xxivList of Figures --- p.xxviiiList of Tables --- p.xxxChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation and Objectives --- p.1Chapter 1.2 --- Subjective Perceptual Quality Assessment --- p.5Chapter 1.3 --- Objective Perceptual Quality Assessment --- p.10Chapter 1.3.1 --- Visual Modelling Approach --- p.10Chapter 1.3.2 --- Engineering Modelling Approach --- p.15Chapter 1.3.3 --- Perceptual Subjective Quality Databases --- p.19Chapter 1.3.4 --- Performance Evaluation --- p.21Chapter 1.4 --- Thesis Contributions --- p.22Chapter 1.5 --- Organization of the Thesis --- p.24Chapter I --- Full Reference Quality Assessment --- p.26Chapter 2 --- Full Reference Image Quality Assessment --- p.27Chapter 2.1 --- Visual Horizontal Effect for Image Quality Assessment --- p.27Chapter 2.1.1 --- Introduction --- p.27Chapter 2.1.2 --- Proposed Image Quality Assessment Framework --- p.28Chapter 2.1.3 --- Experimental Results --- p.34Chapter 2.1.4 --- Conclusion --- p.36Chapter 2.2 --- Image Compression via Adaptive Block-Based Super-Resolution Directed Down-Sampling --- p.37Chapter 2.2.1 --- Introduction --- p.37Chapter 2.2.2 --- The Proposed Image Compression Framework --- p.38Chapter 2.2.3 --- Experimental Results --- p.42Chapter 2.2.4 --- Conclusion --- p.45Chapter 3 --- Full Reference Video Quality Assessment --- p.46Chapter 3.1 --- Adaptive Block-size Transform based Just-Noticeable Dfference Model for Visual Signals --- p.46Chapter 3.1.1 --- Introduction --- p.46Chapter 3.1.2 --- JND Model based on Transforms of Different Block Sizes --- p.48Chapter 3.1.3 --- Selection Strategy Between Transforms of Different Block Sizes --- p.53Chapter 3.1.4 --- JND Model Evaluation --- p.56Chapter 3.1.5 --- Conclusion --- p.60Chapter 3.2 --- Perceptual Quality Assessment --- p.60Chapter 3.2.1 --- Experimental Results --- p.62Chapter 3.2.2 --- Conclusion --- p.64Chapter 3.3 --- Motion Trajectory Based Visual Saliency for Video Quality Assessment --- p.65Chapter 3.3.1 --- Motion Trajectory based Visual Saliency for VQA --- p.66Chapter 3.3.2 --- New Quaternion Representation (QR) for Each frame --- p.66Chapter 3.3.3 --- Saliency Map Construction by QR --- p.67Chapter 3.3.4 --- Incorporating Visual Saliency with VQAs --- p.68Chapter 3.3.5 --- Experimental Results --- p.69Chapter 3.3.6 --- Conclusion --- p.72Chapter 3.4 --- Perceptual Video Coding --- p.72Chapter 3.4.1 --- Experimental Results --- p.75Chapter 3.4.2 --- Conclusion --- p.76Chapter II --- Reduced Reference Quality Assessment --- p.77Chapter 4 --- Reduced Reference Image Quality Assessment --- p.78Chapter 4.1 --- Introduction --- p.78Chapter 4.2 --- Reorganization Strategy of DCT Coefficients --- p.81Chapter 4.3 --- Relationship Analysis of Intra and Inter RDCT subbands --- p.83Chapter 4.4 --- Reduced Reference Feature Extraction in Sender Side --- p.88Chapter 4.4.1 --- Intra RDCT Subband Modeling --- p.89Chapter 4.4.2 --- Inter RDCT Subband Modeling --- p.91Chapter 4.4.3 --- Image Frequency Feature --- p.92Chapter 4.5 --- Perceptual Quality Analysis in the Receiver Side --- p.95Chapter 4.5.1 --- Intra RDCT Feature Difference Analysis --- p.95Chapter 4.5.2 --- Inter RDCT Feature Difference Analysis --- p.96Chapter 4.5.3 --- Image Frequency Feature Difference Analysis --- p.96Chapter 4.6 --- Experimental Results --- p.98Chapter 4.6.1 --- Efficiency of the DCT Reorganization Strategy --- p.98Chapter 4.6.2 --- Performance of the Proposed RR IQA --- p.99Chapter 4.6.3 --- Performance of the Proposed RR IQA over Each Individual Distortion Type --- p.105Chapter 4.6.4 --- Statistical Significance --- p.107Chapter 4.6.5 --- Performance Analysis of Each Component --- p.109Chapter 4.7 --- Conclusion --- p.111Chapter 5 --- Reduced Reference Video Quality Assessment --- p.113Chapter 5.1 --- Introduction --- p.113Chapter 5.2 --- Proposed Reduced Reference Video Quality Metric --- p.114Chapter 5.2.1 --- Reduced Reference Feature Extraction from Spatial Perspective --- p.116Chapter 5.2.2 --- Reduced Reference Feature Extraction from Temporal Perspective --- p.118Chapter 5.2.3 --- Visual Quality Analysis in Receiver Side --- p.121Chapter 5.3 --- Experimental Results --- p.123Chapter 5.3.1 --- Consistency Test of the Proposed RR VQA over Compressed Video Sequences --- p.124Chapter 5.3.2 --- Consistency Test of the Proposed RR VQA over Video Sequences with Simulated Distortions --- p.126Chapter 5.3.3 --- Performance Evaluation of the Proposed RR VQA on Compressed Video Sequences --- p.129Chapter 5.3.4 --- Performance Evaluation of the Proposed RR VQA on Video Sequences Containing Transmission Distortions --- p.133Chapter 5.3.5 --- Performance Analysis of Each Component --- p.135Chapter 5.4 --- Conclusion --- p.137Chapter III --- Retargeted Visual Signal Quality Assessment --- p.138Chapter 6 --- Image Retargeting Perceptual Quality Assessment --- p.139Chapter 6.1 --- Introduction --- p.139Chapter 6.2 --- Preparation of Database Building --- p.142Chapter 6.2.1 --- Source Image --- p.142Chapter 6.2.2 --- Retargeting Methods --- p.143Chapter 6.2.3 --- Subjective Testing --- p.146Chapter 6.3 --- Data Processing and Analysis for the Database --- p.150Chapter 6.3.1 --- Processing of Subjective Ratings --- p.150Chapter 6.3.2 --- Analysis and Discussion of the Subjective Ratings --- p.153Chapter 6.4 --- Objective Quality Metric for Retargeted Images --- p.162Chapter 6.4.1 --- Quality Metric Performances on the Constructed Image Retargeting Database --- p.162Chapter 6.4.2 --- Subjective Analysis of the Shape Distortion and Content Information Loss --- p.165Chapter 6.4.3 --- Discussion --- p.167Chapter 6.5 --- Conclusion --- p.169Chapter 7 --- Conclusions --- p.170Chapter 7.1 --- Conclusion --- p.170Chapter 7.2 --- Future Work --- p.173Chapter A --- Attributes of the Source Image --- p.176Chapter B --- Retargeted Image Name and the Corresponding Number --- p.179Chapter C --- Source Image Name and the Corresponding Number --- p.183Bibliography --- p.18