We present the results of a recent large-scale subjective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to assess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online.

Abstract-There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.

Video quality assessment (QA) continues to be an important area of research due to the overwhelming number of applications where videos are delivered to humans. In particular, the problem of temporal pooling of quality sores has received relatively little attention. We observe a hysteresis effect in the subjective judgment of timevarying video quality based on measured behavior in a subjective study. Based on our analysis of the subjective data, we propose a hysteresis temporal pooling strategy for QA algorithms. Applying this temporal strategy to pool scores from PSNR, SSIM [1] and MOVIE [2] produces markedly improved subjective quality prediction.

Automatic methods to evaluate the perceptual quality of a digital video sequence have widespread applications wherever the end-user is a human. Several objective video quality assessment (VQA) algorithms exist, whose performance is typically evaluated using the results of a subjective study performed by the video quality experts group (VQEG) in 2000. There is a great need for a free, publicly available subjective study of video quality that embodies state-of-the-art in video processing technology and that is effective in challenging and benchmarking objective VQA algorithms. In this paper, we present a study and a resulting database, known as the LIVE Video Quality Database, where 150 distorted video sequences obtained from 10 different source video content were subjectively evaluated by 38 human observers. Our study includes videos that have been compressed by MPEG-2 and H.264, as well as videos obtained by simulated transmission of H.264 compressed streams through error prone IP and wireless networks. The subjective evaluation was performed using a single stimulus paradigm with hidden reference removal, where the observers were asked to provide their opinion of video quality on a continuous scale. We also present the performance of several freely available objective, full reference (FR) VQA algorithms on the LIVE Video Quality Database. The recent MOtion-based Video Integrity Evaluation (MOVIE) index emerges as the leading objective VQA algorithm in our study, while the performance of the Video Quality Metric (VQM) and the Multi-Scale Structural SIMilarity (MS-SSIM) index is noteworthy. The LIVE Video Quality Database is freely available for download 1 and we hope that our study provides researchers with a valuable tool to benchmark and improve the performance of objective VQA algorithms.

No abstract