JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC

The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized. For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes. In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM. The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

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

Subjective quality evaluation of foveated video coding using audio-visual focus of attention

This paper presents a foveated coding method using audio-visual focus of attention and its evaluation through extensive subjective experiments on both standard definition and high definition sequences. Regarding a sound-emitting region as the location drawing the human attention, the method applies varying quality levels in an image frame according to the distance of a pixel to the identified sound source. Two experiments are presented to prove the efficiency of the method. Experiment 1 examines the validity and effectiveness of the method in comparison to the constant quality coding for high quality conditions. In Experiment 2, the method is compared to the fixed bit rate coding for low quality conditions where coding artifacts are noticeable. The results demonstrate that the foveated coding method provides considerable coding gain without significant quality degradation, but uneven distributions of the coding artifacts (blockiness) by the method are often less preferred than the uniform distribution of the artifacts. Additional interesting findings are also discussed, such as content dependence of the performance of the method, the memory effect in multiple viewings, and the difference in the quality perception for frame size variations

Fast and Efficient Foveated Video Compression Schemes for H.264/AVC Platform

Some fast and efficient foveated video compression schemes for H.264/AVC platform are presented in this dissertation. The exponential growth in networking technologies and widespread use of video content based multimedia information over internet for mass communication applications like social networking, e-commerce and education have promoted the development of video coding to a great extent. Recently, foveated imaging based image or video compression schemes are in high demand, as they not only match with the perception of human visual system (HVS), but also yield higher compression ratio. The important or salient regions are compressed with higher visual quality while the non-salient regions are compressed with higher compression ratio. From amongst the foveated video compression developments during the last few years, it is observed that saliency detection based foveated schemes are the keen areas of intense research. Keeping this in mind, we propose two multi-scale saliency detection schemes. (1) Multi-scale phase spectrum based saliency detection (FTPBSD); (2) Sign-DCT multi-scale pseudo-phase spectrum based saliency detection (SDCTPBSD). In FTPBSD scheme, a saliency map is determined using phase spectrum of a given image/video with unity magnitude spectrum. On the other hand, the proposed SDCTPBSD method uses sign information of discrete cosine transform (DCT) also known as sign-DCT (SDCT). It resembles the response of receptive field neurons of HVS. A bottom-up spatio-temporal saliency map is obtained by linear weighted sum of spatial saliency map and temporal saliency map. Based on these saliency detection techniques, foveated video compression (FVC) schemes (FVC-FTPBSD and FVC-SDCTPBSD) are developed to improve the compression performance further.Moreover, the 2D-discrete cosine transform (2D-DCT) is widely used in various video coding standards for block based transformation of spatial data. However, for directional featured blocks, 2D-DCT offers sub-optimal performance and may not able to efficiently represent video data with fewer coefficients that deteriorates compression ratio. Various directional transform schemes are proposed in literature for efficiently encoding such directional featured blocks. However, it is observed that these directional transform schemes suffer from many issues like ‘mean weighting defect’, use of a large number of DCTs and a number of scanning patterns. We propose a directional transform scheme based on direction-adaptive fixed length discrete cosine transform (DAFL-DCT) for intra-, and inter-frame to achieve higher coding efficiency in case of directional featured blocks.Furthermore, the proposed DAFL-DCT has the following two encoding modes. (1) Direction-adaptive fixed length ― high efficiency (DAFL-HE) mode for higher compression performance; (2) Direction-adaptive fixed length ― low complexity (DAFL-LC) mode for low complexity with a fair compression ratio. On the other hand, motion estimation (ME) exploits temporal correlation between video frames and yields significant improvement in compression ratio while sustaining high visual quality in video coding. Block-matching motion estimation (BMME) is the most popular approach due to its simplicity and efficiency. However, the real-world video sequences may contain slow, medium and/or fast motion activities. Further, a single search pattern does not prove efficient in finding best matched block for all motion types. In addition, it is observed that most of the BMME schemes are based on uni-modal error surface. Nevertheless, real-world video sequences may exhibit a large number of local minima available within a search window and thus possess multi-modal error surface (MES). Hence, the following two uni-modal error surface based and multi-modal error surface based motion estimation schemes are developed. (1) Direction-adaptive motion estimation (DAME) scheme; (2) Pattern-based modified particle swarm optimization motion estimation (PMPSO-ME) scheme. Subsequently, various fast and efficient foveated video compression schemes are developed with combination of these schemes to improve the video coding performance further while maintaining high visual quality to salient regions. All schemes are incorporated into the H.264/AVC video coding platform. Various experiments have been carried out on H.264/AVC joint model reference software (version JM 18.6). Computing various benchmark metrics, the proposed schemes are compared with other existing competitive schemes in terms of rate-distortion curves, Bjontegaard metrics (BD-PSNR, BD-SSIM and BD-bitrate), encoding time, number of search points and subjective evaluation to derive an overall conclusion

Harnessing the Potential of Optical Communications for the Metaverse

The Metaverse is a digital world that offers an immersive virtual experience. However, the Metaverse applications are bandwidth-hungry and delay-sensitive that require ultrahigh data rates, ultra-low latency, and hyper-intensive computation. To cater for these requirements, optical communication arises as a key pillar in bringing this paradigm into reality. We highlight in this paper the potential of optical communications in the Metaverse. First, we set forth Metaverse requirements in terms of capacity and latency; then, we introduce ultra-high data rates requirements for various Metaverse experiences. Then, we put forward the potential of optical communications to achieve these data rate requirements in backbone, backhaul, fronthaul, and access segments. Both optical fiber and optical wireless communication (OWC) technologies, as well as their current and future expected data rates, are detailed. In addition, we propose a comprehensive set of configurations, connectivity, and equipment necessary for an immersive Metaverse experience. Finally, we identify a set of key enablers and research directions such as analog neuromorphic optical computing, optical intelligent reflective surfaces (IRS), hollow core fiber (HCF), and terahertz (THz)

VIDEO PREPROCESSING BASED ON HUMAN PERCEPTION FOR TELESURGERY

Video transmission plays a critical role in robotic telesurgery because of the high bandwidth and high quality requirement. The goal of this dissertation is to find a preprocessing method based on human visual perception for telesurgical video, so that when preprocessed image sequences are passed to the video encoder, the bandwidth can be reallocated from non-essential surrounding regions to the region of interest, ensuring excellent image quality of critical regions (e.g. surgical region). It can also be considered as a quality control scheme that will gracefully degrade the video quality in the presence of network congestion. The proposed preprocessing method can be separated into two major parts. First, we propose a time-varying attention map whose value is highest at the gazing point and falls off progressively towards the periphery. Second, we propose adaptive spatial filtering and the parameters of which are adjusted according to the attention map. By adding visual adaptation to the spatial filtering, telesurgical video data can be compressed efficiently because of the high degree of visual redundancy removal by our algorithm. Our experimental results have shown that with the proposed preprocessing method, over half of the bandwidth can be reduced while there is no significant visual effect for the observer. We have also developed an optimal parameter selecting algorithm, so that when the network bandwidth is limited, the overall visual distortion after preprocessing is minimized

Geometry-based spherical JND modeling for 360∘^\circ display

360$^\circ$ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360$^\circ$ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360$^\circ$ projection and display and extend the popular just noticeable difference (JND) model to spherical JND (SJND). First, we propose a quantitative 2D-JND model by jointly considering spatial contrast sensitivity, luminance adaptation and texture masking effect. In particular, our model introduces an entropy-based region classification and utilizes different parameters for different types of regions for better modeling performance. Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360$^\circ$ display. With this operation, SJND reflects both the characteristics of human vision system and the 360$^\circ$ display. Third, our SJND model is more consistent with user perceptions during subjective test and also shows more tolerance in distortions with fewer bit rates during 360$^\circ$ video compression. To further examine the effectiveness of our SJND model, we embed it in Versatile Video Coding (VVC) compression. Compared with the state-of-the-arts, our SJND-VVC framework significantly reduced the bit rate with negligible loss in visual quality