Based on the Just-Noticeable-Difference (JND) criterion, a subjective video quality assessment (VQA) dataset, called the VideoSet, was constructed recently. In this work, we propose a JND-based VQA model using a probabilistic framework to analyze and clean collected subjective test data. While most traditional VQA models focus on content variability, our proposed VQA model takes both subject and content variabilities into account. The model parameters used to describe subject and content variabilities are jointly optimized by solving a maximum likelihood estimation (MLE) problem. As an application, the new subjective VQA model is used to filter out unreliable video quality scores collected in the VideoSet. Experiments are conducted to demonstrate the effectiveness of the proposed model.
The JND-based VOA methodology provides a new framework for fine-grained video quality scores acquisition. Several JND-based VQA datasets were constructed @cite_16 @cite_1 @cite_13 , and JND location prediction methods were examined in @cite_4 @cite_9 . Being inspired by @cite_6 , we develop a JND-based VQA model that considers subject and content variabilities jointly in this work. Then, we will show that this new method provides a powerful data cleaning tool for JND-based VQA datasets.
@cite_4 @cite_9 @cite_1 @cite_6 @cite_16 @cite_13
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Abstract A new methodology to measure coded image video quality using the just-noticeable-difference (JND) idea was proposed in (2015). Several small JND-based image video quality datasets were released by the Media Communications Lab at the University of Southern California in (2016) and (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920 × 1080 , 1280 × 720 , 960 × 540 and 640 × 360 ). For each of the 880 video clips, we encode it using the H.264 AVC codec with QP = 1 , … , 51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (, 2016 [4]). 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. It is generally recognized that severe video distortions that are transient in space and or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database. A compressed video quality assessment dataset based on the just noticeable difference (JND) model, called MCL-JCV, is recently constructed and released. In this work, we explain its design objectives, selected video content and subject test procedures. Then, we conduct statistical analysis on collected JND data. We compute the difference between every two adjacent JND points and propose an outlier detection algorithm to remove unreliable data. We also show that each JND difference group can be well approximated by a normal distribution so that we can adopt the Gaussian mixture model (GMM) to characterize the distribution of multiple JND points. Finally, it is demonstrated by experimental results that the proposed JND analysis performed in the difference domain, called the D-method, achieves a lower BIC (Bayesian information criteria) value than the previously proposed G-method.