An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Investigating Potential Combinations of Visual Features towards Improvement of Full-Reference and No-Reference Image Quality Assessment

Objective assessment of image quality is the process of automatic assignment of a scalar score to an image such that the rating or score corresponds to the score provided by the Human Visual System (HVS). Despite extensive studies since the last two decades, it remains a challenging problem in image processing due to the presence of different types of distortions and limited knowledge of the HVS. Existing approaches for assessing the perceptual quality of images have relied on a number of methodologies that directly apply known properties of the HVS, construct hypotheses considering the HVS as a blackbox and use hybrid approaches that apply both of the techniques. All of these methodologies have relied on different types of visual features for Image Quality Assessment (IQA). In this dissertation, we have studied the problem of different types of IQA from the feature extraction point of view and showed that effective combinations of simple visual features can be used to develop IQA approaches having competitive performance with the state-of-the-art. Our work is divided into four parts each having the final goal to bring about performance improvement in the areas of Full-Reference (FR) and No-Reference (NR)-IQA. We have gradually moved from FR to NR-IQA in the works presented in this dissertation. First, we propose improvements in two existing FR-IQA techniques by introducing changes in the features used. Next, we propose a new FR-IQA technique by extracting image saliency as global features and combining them with the local features of gradient and variance to improve the performance. For NR-IQA, we propose a novel technique for sharpness detection in natural images using simple features. The performance of this method provides improvement over the existing methods. After working with the specific purpose NR-IQA, we propose a general purpose technique using suitable features such that no training with pristine or distorted images or subjective quality scores is required. This technique, despite having no reliance on training, provides competitive performance with the state-of-the-art techniques. The main contribution of the dissertation lies in identification and analysis of effective features and their combinations for improving three different sub-areas of IQA

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.Comment: 11 pages, 9 figures, Accepted by IEEE Transactions on Geoscience and Remote Sensing 2021, project: https://rmcong.github.io/proj_RRNet.htm

Visual working memory in immersive visualization: a change detection experiment and an image-computable model

Visual working memory (VWM) is a cognitive mechanism essential for interacting with the environment and accomplishing ongoing tasks, as it allows fast processing of visual inputs at the expense of the amount of information that can be stored. A better understanding of its functioning would be beneficial to research fields such as simulation and training in immersive Virtual Reality or information visualization and computer graphics. The current work focuses on the design and implementation of a paradigm for evaluating VWM in immersive visualization and of a novel image-based computational model for mimicking the human behavioral data of VWM. We evaluated the VWM at the variation of four conditions: set size, spatial layout, visual angle (VA) subtending stimuli presentation space, and observation time. We adopted a full factorial design and analysed participants' performances in the change detection experiment. The analysis of hit rates and false alarm rates confirms the existence of a limit of VWM capacity of around 7 & PLUSMN; 2 items, as found in the literature based on the use of 2D videos and images. Only VA and observation time influence performances (p<0.0001). Indeed, with VA enlargement, participants need more time to have a complete overview of the presented stimuli. Moreover, we show that our model has a high level of agreement with the human data, r>0.88 (p<0.05)

Video Salient Object Detection via Fully Convolutional Networks

This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps)

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

Predictive Model of Driver\u27s Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems

Over the last few years, Advanced Driver Assistance Systems (ADAS) have been shown to significantly reduce the number of vehicle accidents. Accord- ing to the National Highway Traffic Safety Administration (NHTSA), driver errors contribute to 94% of road collisions. This research aims to develop a predictive model of driver eye fixation by analyzing the driver eye and head information (cephalo-ocular) for maneuver prediction in an Advanced Driving Assistance System (ADAS). Several ADASs have been developed to help drivers to perform driving tasks in complex environments and many studies were conducted on improving automated systems. Some research has relied on the fact that the driver plays a crucial role in most driving scenarios, recognizing the driver’s role as the central element in ADASs. The way in which a driver monitors the surrounding environment is at least partially descriptive of the driver’s situation awareness. This thesis’s primary goal is the quantitative and qualitative analysis of driver behavior to determine the relationship between driver intent and actions. The RoadLab initiative provided an instrumented vehicle equipped with an on-board diagnostic system, an eye-gaze tracker, and a stereo vision system for the extraction of relevant features from the driver, the vehicle, and the environment. Several driver behavioral features are investigated to determine whether there is a relevant relation between the driver’s eye fixations and the prediction of driving maneuvers