Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement

Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents. Compared with conventional methods which are relied on hand-crafted features, deep learning oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions, we adopt the encoder-decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types

Perceptual Quality-of-Experience of Stereoscopic 3D Images and Videos

With the fast development of 3D acquisition, communication, processing and display technologies, automatic quality assessment of 3D images and videos has become ever important. Nevertheless, recent progress on 3D image quality assessment (IQA) and video quality assessment (VQA) remains limited. The purpose of this research is to investigate various aspects of human visual quality-of-experience (QoE) when viewing stereoscopic 3D images/videos and to develop objective quality assessment models that automatically predict visual QoE of 3D images/videos. Firstly, we create a new subjective 3D-IQA database that has two features that are lacking in the literature, i.e., the inclusion of both 2D and 3D images, and the inclusion of mixed distortion types. We observe strong distortion type dependent bias when using the direct average of 2D image quality to predict 3D image quality. We propose a binocular rivalry inspired multi-scale model to predict the quality of stereoscopic images and the results show that the proposed model eliminates the prediction bias, leading to significantly improved quality predictions. Second, we carry out two subjective studies on depth perception of stereoscopic 3D images. The first one follows a traditional framework where subjects are asked to rate depth quality directly on distorted stereopairs. The second one uses a novel approach, where the stimuli are synthesized independent of the background image content and the subjects are asked to identify depth changes and label the polarities of depth. Our analysis shows that the second approach is much more effective at singling out the contributions of stereo cues in depth perception. We initialize the notion of depth perception difficulty index (DPDI) and propose a novel computational model for DPDI prediction. The results show that the proposed model leads to highly promising DPDI prediction performance. Thirdly, we carry out subjective 3D-VQA experiments on two databases that contain various asymmetrically compressed stereoscopic 3D videos. We then compare different mixed-distortions asymmetric stereoscopic video coding schemes with symmetric coding methods and verify their potential coding gains. We propose a model to account for the prediction bias from using direct averaging of 2D video quality to predict 3D video quality. The results show that the proposed model leads to significantly improved quality predictions and can help us predict the coding gain of mixed-distortions asymmetric video compression. Fourthly, we investigate the problem of objective quality assessment of Multi-view-plus-depth (MVD) images, with a main focus on the pre- depth-image-based-rendering (pre-DIBR) case. We find that existing IQA methods are difficult to be employed as a guiding criterion in the optimization of MVD video coding and transmission systems when applied post-DIBR. We propose a novel pre-DIBR method based on information content weighting of both texture and depth images, which demonstrates competitive performance against state-of-the-art IQA models applied post-DIBR

Providing 3D video services: the challenge from 2D to 3DTV quality of experience

Recently, three-dimensional (3D) video has decisively burst onto the entertainment industry scene, and has arrived in households even before the standardization process has been completed. 3D television (3DTV) adoption and deployment can be seen as a major leap in television history, similar to previous transitions from black and white (B&W) to color, from analog to digital television (TV), and from standard definition to high definition. In this paper, we analyze current 3D video technology trends in order to define a taxonomy of the availability and possible introduction of 3D-based services. We also propose an audiovisual network services architecture which provides a smooth transition from two-dimensional (2D) to 3DTV in an Internet Protocol (IP)-based scenario. Based on subjective assessment tests, we also analyze those factors which will influence the quality of experience in those 3D video services, focusing on effects of both coding and transmission errors. In addition, examples of the application of the architecture and results of assessment tests are provided

SUR-FeatNet: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Feature Learning

The file attached to this record is the author's final peer reviewed version.The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson-Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays

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

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Naturalistic depth perception and binocular vision

Humans continuously move both their eyes to redirect their foveae to objects at new depths. To correctly execute these complex combinations of saccades, vergence eye movements and accommodation changes, the visual system makes use of multiple sources of depth information, including binocular disparity and defocus. Furthermore, during development, both fine-tuning of oculomotor control as well as correct eye growth are likely driven by complex interactions between eye movements, accommodation, and the distributions of defocus and depth information across the retina. I have employed photographs of natural scenes taken with a commercial plenoptic camera to examine depth perception while varying perspective, blur and binocular disparity. Using a gaze contingent display with these natural images, I have shown that disparity and peripheral blur interact to modify eye movements and facilitate binocular fusion. By decoupling visual feedback for each eye, I have found it possible to induces both conjugate and disconjugate changes in saccadic adaptation, which helps us understand to what degree the eyes can be individually controlled. To understand the aetiology of myopia, I have developed geometric models of emmetropic and myopic eye shape, from which I have derived psychophysically testable predictions about visual function. I have then tested the myopic against the emmetropic visual system and have found that some aspects of visual function decrease in the periphery at a faster rate in best-corrected myopic observers than in emmetropes. To study the effects of different depth cues on visual development, I have investigated accommodation response and sensitivity to blur in normal and myopic subjects. This body of work furthers our understanding of oculomotor control and 3D perception, has applied implications regarding discomfort in the use of virtual reality, and provides clinically relevant insights regarding the development of refractive error and potential approaches to prevent incorrect emmetropization

Spatial Displays and Spatial Instruments

The conference proceedings topics are divided into two main areas: (1) issues of spatial and picture perception raised by graphical electronic displays of spatial information; and (2) design questions raised by the practical experience of designers actually defining new spatial instruments for use in new aircraft and spacecraft. Each topic is considered from both a theoretical and an applied direction. Emphasis is placed on discussion of phenomena and determination of design principles