Video Quality Model based on a spatiotemporal features extraction for H.264-coded HDTV sequences

International audienceAs a contribution to the design of an objective quality metric in the specific context of High Definition Television (HDTV), this paper proposes a video quality evaluation model. A spatio-temporal segmentation of sequences provide features used with the bitrate to predict the subjective evaluation of the H.264-distorted sequences. In addition, subjective tests have been conducted to provide the mean observer's quality appreciation and assess the model against reality. Existing video quality algorithms have been compared to our model. They are outperformed on every performance criterion

Towards the next generation of video and image quality metrics: Impact of display, resolution, contents and visual attention in subjective assessment

International audienceTwo decades of research in video and image quality assessment has led to the design of subjective assessment protocols and objective metrics. In order to get good performances, most of research works have restricted their focus of interest on SD format or below and on distortion stemming from coding artifacts or transmission error. Considering up-coming services such as HDTV or scalable video coding, next generation of quality metric should take into account more factors that affect the end user quality of experience. In this paper, a review of factors is proposed considering subjective quality assessment. The four studied factors include display, resolution, content and visual attention. Each factor reveals open issues in quality assessment

On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric

The task of extracting objects in video sequences emerges in many applications such as object-based video coding (e.g., MPEG-4) and content-based video indexing and retrieval (e.g., MPEG-7). The MPEG-4 standard provides specifications for the coding of video objects, but does not address the problem of how to extract foreground objects in image sequences. Therefore, for specific applications, evaluating the quality of foreground/background segmentation results is necessary to allow for an appropriate selection of segmentation algorithms and for tuning their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, formal psychophysical experiments evaluating the quality of different video foreground object segmentation results have not yet been conducted. In this paper, a generic framework for both subjective and objective segmentation quality evaluation is presented. An objective quality assessment method for segmentation evaluation is derived on the basis of perceptual factors through subjective experiments. The performance of the proposed method is shown on different state-of-the-art foreground/background segmentation algorithms and our method is compared to other objective methods which do not include perceptual factors. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed metric to meet the specificity of different segmentation applications e.g., video compression, video surveillance and mixed reality. Experimental results confirm the efficiency of the proposed approach

On Evaluating Video Object Segmentation Quality: A Perceptually driven Objective Metric

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no formal psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against state-of-the-art. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed objective metric to meet the specificity of different segmentation applications such as video compression and mixed reality. Experimental results confirm the efficiency of the proposed approach

On Evaluating Metrics For Video Segmentation Algorithms

Evaluation is a central issue in the design, implementation, and performance assessment of all systems. Recently, a number of metrics have been proposed to assess the performance of segmentation algorithms for image and video data. This paper provides an overview of state of the art metrics proposed so-far, and introduces a new and efficient such metric. Doing so, subjective experiments are carried out to derive a perceptual metric. As a result, it also provides a comparison of performance of segmentation assessment metrics for different video object segmentation techniques

A Framework for Evaluating Video Object Segmentation Algorithms

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against an existing approach. Moreover, on the basis of subjective results, perceptual factors are introduced into the novel objective metric to meet the specificity of different segmentation applications such as video compression. Experimental results confirm the efficiency of the proposed evaluation criteria

Application Dependent Video Segmentation Evaluation - A Case Study for Video Surveillance

Evaluation of the performance of video segmentation algorithms is important in both theoretical and practical considerations. This paper addresses the problem of video segmentation assessment, through both subjective and objective approaches, for the specific application of video surveillance. After an overview of the state of the art technique in video segmentation objective evaluation metrics, a general framework is proposed to cope with application dependent evaluation assessment. Finally, the performance of the proposed scheme is compared to state of the art technique and various conclusions are drawn