Video quality

About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.

Measurement of visual impairment scales for digital video

Abstract: The study of subjective visual quality, and the development of computed quality metrics, require accurate and meaningful measurement of visual impairment. A natural unit for impairment is the JND (just-noticeable-difference). In many cases, what is required is a measure of an impairment scale, that is, the growth of the subjective impairment, in JNDs, as some physical parameter (such as amount of artifact) is increased. Measurement of sensory scales is a classical problem in psychophysics. In the method of pair comparison, each trial consists of a pair of samples and the observer selects the one perceived to be greater on the relevant scale. This may be regarded as an extension of the method of forced-choice: from measurement of threshold (one JND), to measurement of the larger sensory scale (multiple JNDs). While simple for the observer, pair comparison is inefficient because if all samples are compared, many comparisons will be uninformative. In general, samples separated by about 1 JND are most informative. We have developed an efficient adaptive method for selection of sample pairs. As with the QUEST adaptive threshold procedure[1], the method is based on Bayesian estimation of the sensory scale after each trial. We call the method Efficient Adaptive Scale Estimation, or EASE ("to make less painful"). We have used the EASE method to measure impairment scales for digital video. Each video was derived from an original source (SRC) by the addition of a particular artifact, produced by a particular codec at a specific bit rate, called a hypothetical reference circuit (HRC). Different amounts of artifact were produced by linear combination of the source and compressed videos. On each pair-comparison trial the observer selected which of two sequences, containing different amounts of artifact, appeared more impaired. The scale is estimated from the pair comparison data using a maximum likelihood method. At the top of the scale, when all of the artifact is present, the scale value is the total number of JNDs corresponding to that SRC/HRC condition. We have measured impairment scales for 25 video sequences, derived from five SRCs combined with each of five HRCs. We find that EASE is a reliable method for measuring impairment scales and JNDs for processed video sequences. We have compared our JND measurements with mean opinion scores for the same sequences obtained at one viewing distance using the DSCQS method by the Video Quality Experts Group (VQEG), and we find that the two measures are highly correlated. The advantages of the JND measurements are that they are in absolute and meaningful units and are unlikely to be subject to context effects. We note that JND measurements offer a means of creating calibrated artifact samples, and of testing and calibrating video quality models. 1. BACKGROUND 1.1. Need for accurate subjective measures of video quality The design and use of digital video systems entail difficult tradeoffs amongst various quantities, of which the two most important are cost and visual quality. While there is no difficulty in measuring cost, beauty remains locked in the eye of the beholder. However in recent years a number of computational metrics have been developed which purport to measure video quality or video impairment. Metrics of this sort would be very valuable in providing a means for automatically specifying, monitoring, and optimizing the visual quality of digital video.

Personalized and mobile digital TV applications

Abstract: The introduction of mobile and broadband networks in complement to the existing satellite, cable, and terrestrial platforms, opens new opportunities for interactive TV (ITV) applications In addition, the widespread adoption of multimedia computing has enabled the processing of TV content on personal devices such as mobile phones and PCs The above developments raise novel issues and require the adoption of new multimedia standards and application frameworks In particular, the explosion in the amount of available TV channels over digital television platforms (broadcast or internet protocol) makes searching and locating interesting content a cumbersome task In this context, personalization research is concerned with the adaptation of content (eg movies, news, advertisements) Personalization is achieved with the employment of algorithms and data collection schemes that predict and recommend to television viewers content that match their interests In addition, the distribution of TV content to mobile devices over broadband wireless raises the issue of video quality Video quality depends on many aspects of the video encoding systems, such as bit rate and algorithms that model human perception of video on small screens In this article, we examine contemporary research in personalized and mobile digital TV applications Moreover, we present a critical survey of the most prominent research and provide directions for further research in personalized and mobile digital TV (DTV) applications

A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

Abstract: We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR).

Streaming video over HTTP with consistent quality

Abstract: In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.

Quality of experience for 3D video streaming

Abstract: New 3D video applications and services are emerging to fulfill increasing user demand. This effort is well supported by the increasing 3D video content including user generated content (e.g., through 3D capture/display enabled mobile phones), technological advancements (e.g., HD 3D video capture and processing methods), affordable 3D displays, and standardization activities. However, not much attention has been given to how these technologies, along the end-to-end chain from content capture to display, affect user perception and whether the overall experience of 3D video users is satisfactory or not. 3D video streaming also introduces artifacts on the reconstructed 3D video at the receiver end, leading to inferior quality and user experience. In this article we present and discuss in detail how artifacts introduced during 3D video streaming affect the end-user perception and how we could use realtime quality evaluation methodologies to overcome these effects. The observations presented can underpin the design of future QoE-aware 3D video streaming systems.