Video Quality Assessment Metrics Research Articles

Novel Spatio-Temporal Structural Information Based Video Quality Metric

Video quality assessment (VQA) is very important for many video processing applications, e.g., compression, archiving, restoration, and enhancement. An ideal video quality metric should achieve consistency between video distortion prediction and psychological perception of human visual system. Different from the quality assessment of single images, motion information and temporal distortion should be carefully considered for VQA. Most of previous VQA algorithms deal with the motion information through two ways: either incorporating motion characteristics into a temporal weighting scheme to account for their affects on the spatial distortion, or modeling the temporal distortion and spatial distortion independently. Optical flows need to be estimated in the two ways. In this paper, we propose a different methodology to deal with the motion information. Instead of explicitly calculating the optical flow and independently modeling the temporal distortion, both the spatial edge features and temporal motion characteristics are accounted for by some structural features in the localized spacetime regions. We propose to represent the structural information by two descriptors extracted from the 3-D structure tensors, which are the largest eigenvalue as well as its corresponding eigenvector. Experimental results on LIVE database and VQEG FR-TV Phase-I database show that the proposed VQA metric is competitive with state-of-the-art VQA metrics, while keeping relatively low computing complexity.

No-reference video quality assessment in the compressed domain

In this paper, a novel no-reference video quality assessment algorithm in the compressed domain is introduced. The proposed algorithm takes into account of three key factors; the quantization parameter, the motion, and the bit allocation factor which are calculated using the information extracted from the compressed bitstream. Characteristics of human visual system are also taken into consideration. In contrast to conventional video quality assessment metrics which measure the video quality frame by frame, the video sequence is processed by this algorithm as a whole. The proposed algorithm has been tested on the H.264/AVC bitstreams in LIVE Video Quality Database. Furthermore, bitstreams encoded by JM 17.0 with different configurations are also tested to examine the generality. The experiment results show that the proposed algorithm has better performance than many conventional methods in terms of both the prediction accuracy and the computation complexity.

A packet-layer video quality assessment model with spatiotemporal complexity estimation

A packet-layer video quality assessment (VQA) model is a lightweight model that predicts the video quality impacted by network conditions and coding configuration for application scenarios such as video system planning and in-service video quality monitoring. It is under standardization in ITU-T Study Group (SG) 12. In this article, we first differentiate the requirements for VQA model from the two application scenarios, and state the argument that the dataset for evaluating the quality monitoring model should be more challenging than that for system planning model. Correspondingly, different criteria and approaches are used for constructing the test datasets, for system planning (dataset-1) and for video quality monitoring (dataset-2), respectively. Further, we propose a novel video quality monitoring model by estimating the spatiotemporal complexity of video content. The model takes into account the interactions among content features, the error concealment effectiveness, and error propagation effects. Experiment results demonstrate that the proposed model achieves robust performance improvement compared with the existing peer VQA metrics on both dataset-1 and dataset-2. It is noted that on the more challenging dataset-2 for video quality monitoring, we obtain a large increase in Pearson correlation from 0.75 to 0.92 and a decrease in the modified RMSE from 0.41 to 0.19.

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.

Atherosclerotic Plaque Ultrasound Video Encoding, Wireless Transmission, and Quality Assessment Using H.264

We propose a unifying framework for efficient encoding, transmission, and quality assessment of atherosclerotic plaque ultrasound video. The approach is based on a spatially varying encoding scheme, where video-slice quantization parameters are varied as a function of diagnostic significance. Video slices are automatically set based on a segmentation algorithm. They are then encoded using a modified version of H.264/AVC flexible macroblock ordering (FMO) technique that allows variable quality slice encoding and redundant slices (RSs) for resilience over error-prone transmission channels. We evaluate our scheme on a representative collection of ten ultrasound videos of the carotid artery for packet loss rates up to 30%. Extensive simulations incorporating three FMO encoding methods, different quantization parameters, and different packet loss scenarios are investigated. Quality assessment is based on a new clinical rating system that provides independent evaluations of the different parts of the video (subjective). We also use objective video-quality assessment metrics and estimate their correlation to the clinical quality assessment of plaque type. We find that some objective quality assessment measures computed over the plaque video slices gave very good correlations to mean opinion scores (MOSs). Here, MOSs were computed using two medical experts. Experimental results show that the proposed method achieves enhanced performance in noisy environments, while at the same time achieving significant bandwidth demands reductions, providing transmission over 3G (and beyond) wireless networks.

A user-perceived video quality assessment metric using inter-frame redundancy

A user-perceived video quality assessment metric using inter-frame redundancy

A Video Quality Assessment Metric Based on Human Visual System

It is important for practical application to design an effective and efficient metric for video quality. The most reliable way is by subjective evaluation. Thus, to design an objective metric by simulating human visual system (HVS) is quite reasonable and available. In this paper, the video quality assessment metric based on visual perception is proposed. Three-dimensional wavelet is utilized to decompose video and then extract features to mimic the multichannel structure of HVS. Spatio-temporal contrast sensitivity function (S-T CSF) is employed to weight coefficient obtained by three-dimensional wavelet to simulate nonlinearity feature of the human eyes. Perceptual threshold is exploited to obtain visual sensitive coefficients after S-T CSF filtered. Visual sensitive coefficients are normalized representation and then visual sensitive errors are calculated between reference and distorted video. Finally, temporal perceptual mechanism is applied to count values of video quality for reducing computational cost. Experimental results prove the proposed method outperforms the most existing methods and is comparable to LHS and PVQM.

Considering Temporal Variations of Spatial Visual Distortions in Video Quality Assessment

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.