The video quality assessment (VQA) technology has attracted a lot of attention in recent years due to an increasing demand of video streaming services. Existing VQA methods are designed to predict video quality in terms of the mean opinion score (MOS) calibrated by humans in subjective experiments. However, they cannot predict the satisfied user ratio (SUR) of an aggregated viewer group. Furthermore, they provide little guidance to video coding parameter selection, e.g. the Quantization Parameter (QP) of a set of consecutive frames, in practical video streaming services. To overcome these shortcomings, the just-noticeable-difference (JND) based VQA methodology has been proposed as an alternative. It is observed experimentally that the JND location is a normally distributed random variable. In this work, we explain this distribution by proposing a user model that takes both subject variabilities and content variabilities into account. This model is built upon user’s capability to discern the quality difference between video clips encoded with different QPs. Moreover, it analyzes video content characteristics to account for inter-content variability. The proposed user model is validated on the data collected in the VideoSet. It is demonstrated that the model is flexible to predict SUR distribution of a specific user group.
A theoretical subject model @cite_3 was proposed to model the three major factors that influence MOS accuracy: subject bias, subject inaccuracy, and stimulus scoring difficulty. It was reported that the distribution of these three factors spanned about $ 25 rating scale. Especially, the subject error terms explained previously observed inconsistencies both within a single subject's data and also the lab-to-lab differences. A perceptually weighted rank correlation indicator @cite_17 was proposed, which rewarded the capability of corrected ranking high-quality images and suppressed the attention towards insensitive rank mistakes. A generative model @cite_10 was proposed to jointly recover content and subject factors by solving a maximum likelihood estimation problem. However, these models were proposed for the traditional MOS-based approaches.
@cite_10 @cite_3 @cite_17
2177315755 2611862713 1974013408 1989779308
How accurately are people able to use the absolute category rating (ACR) 5-level scale? Put another way, how repeatable are an individual subject’s scores? Several subjective experiments have asked subjects to rate the same sequences a couple of times. Analyses indicate that none of the subjects exactly repeated their prior scores for these sequences. We would like to better understand this imperfection. This paper uses ACR subjective video quality tests to explore the precision of subjective ratings. To make formal measurements possible, we propose a theoretical subject model that is the main contribution of this paper. The proposed subject model indicates three major factors that influence accuracy: subject bias, subject inaccuracy, and stimulus scoring difficulty. These appear to be separate random effects and their existence is a reason why none of the subjects were able to perfectly repeat scores. There are three key consequences. First, subject scoring behavior includes a random component that spans approximately half of the rating scale. Second, the sensitivity and accuracy of most subjective analyses can be improved if the subject scores are normalized by removing subject bias. Third, to some extent, multiple subjects can be replaced with a single subject who rates each sequence multiple times. Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8 CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5 boost. This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described. Abstract For the last decades, computer-based visual attention models aiming at automatically predicting human gaze on images or videos have exponentially increased. Even if several families of methods have been proposed and a lot of words like centre-surround difference, contrast, rarity, novelty, redundancy, irregularity, surprise or compressibility have been used to define those models, they are all based on the same and unique idea of information innovation in a given context . In this paper, we propose a novel saliency prediction model, called RARE2012, which selects information worthy of attention based on multi-scale spatial rarity. RARE2012 is then evaluated using two complementary metrics, the Normalized Scanpath Saliency (NSS) and the Area Under the Receiver Operating Characteristic (AUROC) against 13 recently published saliency models. It is shown to be the best for NSS metric and second best for AUROC metric on three publicly available datasets (Toronto, Koostra and Jian Li). Finally, based on an additional comparative statistical analysis and the effect-size Hedge' g ⁎ measure, RARE2012 outperforms, at least slightly, the other models while considering both metrics on the three databases as a whole.