12 research outputs found
Evaluating the Role of Content in Subjective Video Quality Assessment
Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA
Study of saliency in objective video quality assessment
Reliably predicting video quality as perceived by humans remains challenging and is of high practical relevance. A significant research trend is to investigate visual saliency and its implications for video quality assessment. Fundamental problems regarding how to acquire reliable eye-tracking data for the purpose of video quality research and how saliency should be incorporated in objective video quality metrics (VQMs) are largely unsolved. In this paper, we propose a refined methodology for reliably collecting eye-tracking data, which essentially eliminates bias induced by each subject having to view multiple variations of the same scene in a conventional experiment. We performed a large-scale eye-tracking experiment that involved 160 human observers and 160 video stimuli distorted with different distortion types at various degradation levels. The measured saliency was integrated into several best known VQMs in the literature. With the assurance of the reliability of the saliency data, we thoroughly assessed the capabilities of saliency in improving the performance of VQMs, and devised a novel approach for optimal use of saliency in VQMs. We also evaluated to what extent the state-of-the-art computational saliency models can improve VQMs in comparison to the improvement achieved by using “ground truth” eye-tracking data. The eye-tracking database is made publicly available to the research community
No reference quality assessment of stereo video based on saliency and sparsity
With the popularity of video technology, stereoscopic video quality assessment (SVQA) has become increasingly important. Existing SVQA methods cannot achieve good performance because the videos' information is not fully utilized. In this paper, we consider various information in the videos together, construct a simple model to combine and analyze the diverse features, which is based on saliency and sparsity. First, we utilize the 3-D saliency map of sum map, which remains the basic information of stereoscopic video, as a valid tool to evaluate the videos' quality. Second, we use the sparse representation to decompose the sum map of 3-D saliency into coefficients, then calculate the features based on sparse coefficients to obtain the effective expression of videos' message. Next, in order to reduce the relevance between the features, we put them into stacked auto-encoder, mapping vectors to higher dimensional space, and adding the sparse restraint, then input them into support vector machine subsequently, and finally, get the quality assessment scores. Within that process, we take the advantage of saliency and sparsity to extract and simplify features. Through the later experiment, we can see the proposed method is fitting well with the subjective scores
A multimodal approach for the automatic assessment of viewer subjective perception of Youtube videos
This research project is focused on the understanding of how saliency could influence the impression perceived by a viewer of a video. However, this perception cannot be perfectly assessed since some inherent bias is inevitable. This problem is an existing error that should be understood and taken into account in any statistical analysis or experiments. For instance, and focused on this research, the perception of a viewer can be affected by tendencies, inclinations or feelings of the individual (Attentional bias). In order to lessen the impact of this issue it is important to define a simplified research field, limiting tendencies or feelings of the viewers. Due this, car commercials were chosen as the video domain for the study. These and other following choices were previously stablished by Alejandro Hernández GarcĂa in his project called Aesthetics Assessment of Videos through Visual Descriptors and Automatic Polarity Annotation which will be named several times in this research.
This car commercial database was extracted from YouTube and was simplified obtaining finally a collection of 138 car commercial videos. This collection will be used for this project too and will be the domain for the proposed computational model.
This way, we will demonstrate the impact of the saliency in subjective perception, focusing this assumption on car commercial videos extracted from YouTube. As consequence of this assumption, saliency also would modify the video ratings in this platformIngenierĂa de Sistemas Audiovisuale
Avaliação de qualidade de vĂdeo utilizando modelo de atenção visual baseado em saliĂŞncia
Video quality assessment plays a key role in the video processing and communications applications. An ideal video quality metric shall ensure high correlation between the video distortion prediction and the perception of the Human Visual System. This work proposes the use of visual attention models with bottom-up approach based on saliencies for video qualitty assessment. Three objective metrics are proposed. The first method is a full reference metric based on the structural similarity. The second is a no reference metric based on a sigmoidal model with least squares solution using the Levenberg-Marquardt algorithm and extraction of spatial and temporal features. And, the third is analagous to the last one, but uses the characteristic Blockiness for detecting blocking distortions in the video. The bottom-up approach is used to obtain the salient maps, which are extracted using a multiscale background model based on motion detection. The experimental results show an increase of efficiency in the quality prediction of the proposed metrics using salient model in comparission to the same metrics not using these model, highlighting the no reference proposed metrics that had better results than metrics with reference to some categories of videos.A avaliação de qualidade de vĂdeo possui um papel fundamental no processamento de vĂdeo e em aplicações de comunicação. Uma mĂ©trica de qualidade de vĂdeo ideal deve garantir a alta correlação entre a predição da distorção do vĂdeo e a percepção de qualidade do Sistema Visual Humano. Este trabalho propõe o uso de modelos de atenção visual com abordagem bottom up baseados em saliĂŞncias para avaliação de qualidade de vĂdeo. TrĂŞs mĂ©tricas objetivas de avaliação sĂŁo propostas. O primeiro mĂ©todo Ă© uma mĂ©trica com referĂŞncia completa baseada na estrutura de similaridade. O segundo modelo Ă© uma mĂ©trica sem referĂŞncia baseada em uma modelagem sigmoidal com solução de mĂnimos quadrados que usa o algoritmo de Levenberg-Marquardt e extração de caracterĂsticas espaço-temporais. E, a terceira mĂ©trica Ă© análoga Ă segunda, porĂ©m usa a caracterĂstica Blockiness na detecção de distorções de blocagem no vĂdeo. A abordagem bottom-up Ă© utilizada para obter os mapas de saliĂŞncias que sĂŁo extraĂdos atravĂ©s de um modelo multiescala de background baseado na detecção de movimentos. Os resultados experimentais apresentam um aumento da eficiĂŞncia de predição de qualidade de vĂdeo nas mĂ©tricas que utilizam o modelo de saliĂŞncia em comparação com as respectivas mĂ©tricas que nĂŁo usam este modelo, com destaque para as mĂ©tricas sem referĂŞncia propostas que apresentaram resultados melhores do que mĂ©tricas com referĂŞncia para algumas categorias de vĂdeos