Video quality

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

Energy-Efficient Resource Allocation and Scheduling for Multicast of Scalable Video Over Wireless Networks

Abstract: In this paper, we investigate optimal resource allocation and scheduling for scalable video multicast over wireless networks. The wireless video multicasting is a best-effort service which has limited transmission energy and channel access time. To cater for multi-resolution videos to heterogeneous clients and for channel adaptation, we adopt scalable video coding (SVC) with spatial, temporal and quality scalabilities. Our scalable video multicast system consists of a channel probing stage to gather the channel state information and a transmission stage to multicast videos to clients. We formulate the optimal resource allocation problem by maximizing the video quality of the clients subject to transmission energy and channel access constraints. We show that the problem is a joint optimization of the selection of modulation and coding scheme (MCS), and the transmission power allocation. By imposing a quality-of-service (QoS) constraint on the packet loss rate, we simplify the original problem to a binary knapsack problem which can be solved by a dynamic programming approach. Specifically, we first propose a multicast scheduling scheme based on the quality impact of each SVC layer. Guided by the content-aware multicast scheduling, we optimize the resource allocation for each SVC layer sequentially. Solution at each step takes into account of the channel condition, remaining resources, and client requirements. The proposed scheme is of linear complexity and leads to the maximized video quality for the admitted clients, while satisfying the energy budget and channel access constraints. Experiment results demonstrate that our scheme achieves notable video quality improvements for multicast clients, when compared to the state-of-the-art video multicast method.

AMISP: a complete content-based MPEG-2 error-resilient scheme

Abstract: We address a new error-resilient scheme for broadcast quality MPEG-2 video streams to be transmitted over lossy packet networks. A new scene-complexity adaptive mechanism, namely Adaptive MPEG-2 Information Structuring (AMIS) is introduced. AMIS modulates the number of resynchronization points (i.e., slice headers and intra-coded macroblocks) in order to maximize the perceived video quality, assuming that the encoder is aware of the underlying packetization scheme, the packet loss probability (PLR), and the error-concealment technique implemented at the decoding side. The end-to-end video quality depends both on the encoding quality and the degradation due to data loss. Therefore, AMIS constantly determines the best compromise between the rate allocated to encode pure video information and the rate aiming at reducing the sensitivity to packet loss. Experimental results show that AMIS dramatically outperforms existing structuring techniques, thanks to its efficient adaptivity. We then extend AMIS with a forward-error-correction (FEC)-based protection algorithm to become AMISP. AMISP triggers the insertion of FEC packets in the MPEG-2 video packet stream. Finally, the performances of the AMISP scheme in an MPEG-2 over RTP/UDP/IP scenario are evaluated.

An efficient decoding of CAVLC in H.264/AVC video coding standard

Abstract: In general, a large number of the memory accesses are required to decode the CAVLC in H.264/AVC. This is a serious problem for applications such as a DMB and videophone services because of the considerable amount of power that is consumed in accessing the memory. In order to overcome this problem, we propose an efficient coeff/spl I.bar/token VLD and a new run/spl I.bar/before VLD based on arithmetic operations. The simulation results show that the proposed algorithm achieves an approximate 65-88% savings in memory access without degrading video quality as compared to the conventional CAVLC decoding.

No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications

Abstract: Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.

Color moving pictures quality metric

Abstract: This paper presents a comprehensive quality metric for color moving pictures which is based on a spatio-temporal vision model and on the opponent-colors theory. The metric is used to assess the quality of MPEG compressed video streams and is compared with a grayscale video quality metric.