Video quality

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

Metrics for Evaluating Video Streaming Quality in Lossy IEEE 802.11 Wireless Networks

Abstract: Peak Signal-to-Noise Ratio (PSNR) is the simplest and the most widely used video quality evaluation methodology. However, traditional PSNR calculations do not take the packet loss into account. This shortcoming, which is amplified in wireless networks, contributes to the inaccuracy in evaluating video streaming quality in wireless communications. Such inaccuracy in PSNR calculations adversely affects the development of video communications in wireless networks. This paper proposes a novel video quality evaluation methodology. As it not only considers the PSNR of a video, but also with modifications to handle the packet loss issue, we name this evaluation method MPSNR. MPSNR rectifies the inaccuracies in traditional PSNR computation, and helps us to approximate subjective video quality, Mean Opinion Score (MOS), more accurately. Using PSNR values calculated from MPSNR and simple network measurements, we apply linear regression techniques to derive two specific objective video quality metrics, PSNR-based Objective MOS (POMOS) and Rates-based Objective MOS (ROMOS). Through extensive experiments and human subjective tests, we show that the two metrics demonstrate high correlation with MOS. POMOS takes the averaged PSNR value of a video calculated from MPSNR as the only input. Despite its simplicity, it has a Pearson correlation of 0.8664 with the MOS. By adding a few other simple network measurements, such as the proportion of distorted frames in a video, ROMOS achieves an even higher Pearson correlation (0.9350) with the MOS. Compared with the PSNR metric from the traditional PSNR calculations, our metrics evaluate video streaming quality in wireless networks with a much higher accuracy while retaining the simplicity of PSNR calculation.

No-reference image quality assessment with deep convolutional neural networks

Abstract: The state-of-the-art general-purpose no-reference image or video quality assessment (NR-I/VQA) algorithms usually rely on elaborated hand-crafted features which capture the Natural Scene Statistics (NSS) properties. However, designing these features is usually not an easy problem. In this paper, we describe a novel general-purpose NR-IQA framework which is based on deep Convolutional Neural Networks (CNN). Directly taking a raw image as input and outputting the image quality score, this new framework integrates the feature learning and regression into one optimization process, which provides an end-to-end solution to the NR-IQA problem and frees us from designing hand-crafted features. This approach achieves excellent performance on the LIVE dataset and is very competitive with other state-of-the-art NR-IQA algorithms.

Mobile video streaming using location-based network prediction and transparent handover

Abstract: A well known challenge with mobile video streaming is fluctuating bandwidth. As the client devices move in and out of network coverage areas, the users may experience varying signal strengths, competition for the available resources and periods of network outage. These conditions have a significant effect on video quality. In this paper, we present a video streaming solution for roaming clients that is able to compensate for the effects of oscillating bandwidth through bandwidth prediction and video quality scheduling. We combine our existing adaptive segmented HTTP streaming system with 1) an application layer framework for creating transparent multi-link applications, and 2) a location based QoS information system containing GPS coordinates and accompanying bandwidth measurements, populated through crowd-sourcing. Additionally, we use real-time traffic information to improve the prediction by, for example, estimating the length of a commute route. To evaluate our prototype, we performed real-world experiments using a popular tram route in Oslo, Norway. The client connected to multiple networks, and the results show that our solution increases the perceived video quality significantly. Also, we used simulations to evaluate the potential of aggregating bandwidth along the route.

Cooperative Layered Video Multicast Using Randomized Distributed Space Time Codes

Abstract: With the increased popularity of mobile multimedia services, efficient and robust video multicast strategies are of critical importance. Cooperative communications has been shown to improve the robustness and the data rates for point-to-point transmission. In this paper, a two-hop cooperative transmission scheme for multicast in infrastructure-based networks is used, where multiple relays forward the data simultaneously using randomized distributed space time codes (RDSTC). This randomized cooperative transmission is further integrated with layered video coding and packet level forward error correction (FEC) to enable efficient and robust video multicast. Three different schemes are proposed to find the system operating parameters based on the availability of the channel information at the source station: RDSTC with full channel information, RDSTC with limited channel information, and RDSTC with node count. The performance of these three schemes are compared with rate adaptive direct transmission and conventional multicast that does not use rate adaptation. The results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, all three proposed randomized cooperative schemes outperform both strategies significantly as long as the network has enough nodes. Furthermore, the performance gap between RDSTC with full channel information and RDSTC with limited channel information or node count is relatively small, indicating the robustness of the proposed cooperative multicast system using RDSTC.

Evaluating desktop video conferencing for distance learning

Abstract: Distance learning applications can now make use of networked computers to transmit and display video, audio, and graphics. However, desktop video conferencing systems (DVC) often display degraded images due to bandwidth restrictions and computer processing limitations. The literature on the influence of video parameters such as frame rate and resolution with respect to subjective opinions and human performance is sparse. A two-part study involving a controlled laboratory experiment and a field study evaluation was conducted on technical parameters affecting the suitability of DVC for distance learning. In the laboratory study, three frame rate conditions (1, 6, and 30 frames per second), two resolution conditions (160 × 120 and 320 × 240), and three communication channel conditions were manipulated. Dependent measures included performance on a quiz and subjective satisfaction with the image quality. Results suggest that quiz performance does not suffer under reduced video quality conditions, but subjective satisfaction significantly decreases. The field study employed similar dependent measures and indicates that students in real classroom situations may be less critical of poor video quality than in laboratory settings and confirms the results from the laboratory study in that performance does not suffer. However, the current state-of-the-art of video conferencing technology needs to be improved and configured most effectively to support college teaching at a distance. Guidelines for implementing and using DVC systems in distance learning applications are provided.