FOVQA: Blind Foveated Video Quality Assessment

Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model, called FOVQA, which is based on new models of space-variant natural scene statistics (NSS) and natural video statistics (NVS). Specifically, we deploy a space-variant generalized Gaussian distribution (SV-GGD) model and a space-variant asynchronous generalized Gaussian distribution (SV-AGGD) model of mean subtracted contrast normalized (MSCN) coefficients and products of neighboring MSCN coefficients, respectively. We devise a foveated video quality predictor that extracts radial basis features, and other features that capture perceptually annoying rapid quality fall-offs. We find that FOVQA achieves state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as compared with other leading FIQA / VQA models. we have made our implementation of FOVQA available at: http://live.ece.utexas.edu/research/Quality/FOVQA.zip

Content-prioritised video coding for British Sign Language communication.

Video communication of British Sign Language (BSL) is important for remote interpersonal communication and for the equal provision of services for deaf people. However, the use of video telephony and video conferencing applications for BSL communication is limited by inadequate video quality. BSL is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Accurate natural BSL communication places specific demands on visual media applications which must compress video image data for efficient transmission. Current video compression schemes apply methods to reduce statistical redundancy and perceptual irrelevance in video image data based on a general model of Human Visual System (HVS) sensitivities. This thesis presents novel video image coding methods developed to achieve the conflicting requirements for high image quality and efficient coding. Novel methods of prioritising visually important video image content for optimised video coding are developed to exploit the HVS spatial and temporal response mechanisms of BSL users (determined by Eye Movement Tracking) and the characteristics of BSL video image content. The methods implement an accurate model of HVS foveation, applied in the spatial and temporal domains, at the pre-processing stage of a current standard-based system (H.264). Comparison of the performance of the developed and standard coding systems, using methods of video quality evaluation developed for this thesis, demonstrates improved perceived quality at low bit rates. BSL users, broadcasters and service providers benefit from the perception of high quality video over a range of available transmission bandwidths. The research community benefits from a new approach to video coding optimisation and better understanding of the communication needs of deaf people

Visual perception of content-prioritised sign language video quality.

Video communication systems currently provide poor quality and performance for deaf people using sign language, particularly at low bit rates. Our previous work, involving eye movement tracking experiments and analysis of visual attention mechanisms for sign language, demonstrated a consistent characteristic response which could be exploited to enable optimisation of video coding systems performance by prioritising content for deaf users. This paper describes an experiment designed to test the perceived quality of selectively prioritised video for sign language communication. A series of selectively degraded video clips was shown to individual deaf viewers. Participants subjectively rated the quality of the modified video on a Degradation Category Rating (DCR) scale adapted for sign language users. The results demonstrate the potential to develop content-prioritised coding schemes, based on viewing behaviour, which can reduce bandwidth requirements and provide best quality for the needs of the user. We propose selective quantisation to reduce compression in visually important regions of video images, which require spatial detail for small slow motion detection, and increased compression of regions regarded in peripheral vision where large rapid movements occur in sign language communication

Off-line Foveated Compression and Scene Perception: An Eye-Tracking Approach

With the continued growth of digital services offering storage and communication of pictorial information, the need to efficiently represent this information has become increasingly important, both from an information theoretic and a perceptual point of view. There has been a recent interest to design systems for efficient representation and compression of image and video data that take the features of the human visual system into account. One part of this thesis investigates whether knowledge about viewers' gaze positions as measured by an eye-tracker can be used to improve compression efficiency of digital video; regions not directly looked at by a number of previewers are lowpass filtered. This type of video manipulation is called off-line foveation. The amount of compression due to off-line foveation is assessed along with how it affects new viewers' gazing behavior as well as subjective quality. We found additional bitrate savings up to 50% (average 20%) due to off-line foveation prior to compression, without decreasing the subjective quality. In off-line foveation, it would be of great benefit to algorithmically predict where viewers look without having to perform eye-tracking measurements. In the first part of this thesis, new experimental paradigms combined with eye-tracking are used to understand the mechanisms behind gaze control during scene perception, thus investigating the prerequisites for such algorithms. Eye-movements are recorded from observers viewing contrast manipulated images depicting natural scenes under a neutral task. We report that image semantics, rather than the physical image content itself, largely dictates where people choose to look. Together with recent work on gaze prediction in video, the results in this thesis give only moderate support for successful applicability of algorithmic gaze prediction for off-line foveated video compression