Video quality

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

Considering Temporal Variations of Spatial Visual Distortions in Video Quality Assessment

Abstract: The temporal distortions such as flickering, jerkiness, and mosquito noise play a fundamental part in video quality assessment. A temporal distortion is commonly defined as the temporal evolution, or fluctuation, of the spatial distortion on a particular area which corresponds to the image of a specific object in the scene. Perception of spatial distortions over time can be largely modified by their temporal changes, such as increase or decrease in the distortions, or as periodic changes in the distortions. In this paper, we have designed a perceptual full reference video quality assessment metric by focusing on the temporal evolutions of the spatial distortions. As the perception of the temporal distortions is closely linked to the visual attention mechanisms, we have chosen to first evaluate the temporal distortion at eye fixation level. In this short-term temporal pooling, the video sequence is divided into spatio-temporal segments in which the spatio-temporal distortions are evaluated, resulting in spatio-temporal distortion maps. Afterwards, the global quality score of the whole video sequence is obtained by the long-term temporal pooling in which the spatio-temporal maps are spatially and temporally pooled. Consistent improvement over objective existing video quality assessment methods is observed. Our validation has been realized with a dataset built from video sequences of various contents.

QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

Abstract: The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

Quality Evaluation of Color Plus Depth Map-Based Stereoscopic Video

Abstract: In the near future, many conventional video applications are likely to be replaced by immersive video to provide a sense of ldquobeing there.rdquo This transition is facilitated by the recent advancement of 3D capture, coding, transmission, and display technologies. Stereoscopic video is the simplest form of 3D video available in the literature. ldquoColor plus depth maprdquo based stereoscopic video has attracted significant attention, as it can reduce storage and bandwidth requirements for the transmission of stereoscopic content over communication channels. However, quality assessment of coded video sequences can currently only be performed reliably using expensive and inconvenient subjective tests. To enable researchers to optimize 3D video systems in a timely fashion, it is essential that reliable objective measures are found. This paper investigates the correlation between subjective and objective evaluation of color plus depth video. The investigation is conducted for different compression ratios, and different video sequences. Transmission over Internet protocol (IP) is also investigated. Subjective tests are performed to determine the image quality and depth perception of a range of differently coded video sequences, with packet loss rates ranging from 0% to 20%. The subjective results are used to determine more accurate objective quality assessment metrics for 3D color plus depth video.

A basic multimedia quality model

Abstract: This paper describes two experiments designed to develop a basic multimedia predictive quality metric. In Experiment 1, two head and shoulder audio-video sequences were used for test material. Experiment 2 used one of the head and shoulder sequences from Experiment 1 together with a different, high-motion sequence. In both experiments, subjects assessed the audio quality first, followed by the video quality and finally a third test evaluated multimedia quality. The results of these studies found that human subjects integrate audio and video quality together using a multiplicative rule. A regression analysis using the subjective quality test data from each experiment found that: 1) for head and shoulder content, both modalities contribute significantly to the predictive power of the resultant model, although audio quality is weighted slightly higher than video quality and 2) for high-motion content, video quality is weighted significantly higher than audio quality.

Spatiotemporal Statistics for Video Quality Assessment

Abstract: It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types, which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are first extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression model afterward. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in the 3D-DCT domain that has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; and 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing full-reference VQA and reduced-reference VQA metrics.