NAMA3DS1-COSPAD1: Subjective video quality assessment database on coding conditions introducing freely available high quality 3D stereoscopic sequences

Research in stereoscopic 3D coding, transmission and subjective assessment methodology depends largely on the availability of source content that can be used in cross-lab evaluations. While several studies have already been presented using proprietary content, comparisons between the studies are difficult since discrepant contents are used. Therefore in this paper, a freely available dataset of high quality Full-HD stereoscopic sequences shot with a semiprofessional 3D camera is introduced in detail. The content was designed to be suited for usage in a wide variety of applications, including high quality studies. A set of depth maps was calculated from the stereoscopic pair. As an application example, a subjective assessment has been performed using coding and spatial degradations. The Absolute Category Rating with Hidden Reference method was used. The observers were instructed to vote on video quality only. Results of this experiment are also freely available and will be presented in this paper as a first step towards objective video quality measurement for 3DTV

Stereoscopic video quality assessment using binocular energy

Stereoscopic imaging is becoming increasingly popular. However, to ensure the best quality of experience, there is a need to develop more robust and accurate objective metrics for stereoscopic content quality assessment. Existing stereoscopic image and video metrics are either extensions of conventional 2D metrics (with added depth or disparity information) or are based on relatively simple perceptual models. Consequently, they tend to lack the accuracy and robustness required for stereoscopic content quality assessment. This paper introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. The proposed approach is based on the following three contributions. First, it introduces a novel HVS model extending previous models to include the phenomena of binocular suppression and recurrent excitation. Second, an image quality metric based on the novel HVS model is proposed. Finally, an optimised temporal pooling strategy is introduced to extend the metric to the video domain. Both image and video quality metrics are obtained via a training procedure to establish a relationship between subjective scores and objective measures of the HVS model. The metrics are evaluated using publicly available stereoscopic image/video databases as well as a new stereoscopic video database. An extensive experimental evaluation demonstrates the robustness of the proposed quality metrics. This indicates a considerable improvement with respect to the state-of-the-art with average correlations with subjective scores of 0.86 for the proposed stereoscopic image metric and 0.89 and 0.91 for the proposed stereoscopic video metrics

Quality of experience model for 3DTV

International audienceModern stereoscopic 3DTV brings new QoE (quality of experience) to viewers, which not only enhances the 3D sensation due to the added binocular depth, but may also induce new problems such as visual discomfort. Subjective quality assessment is the conventional method to assess the QoE. However, the conventional perceived image quality concept is not enough to reveal the advantages and the drawbacks of stereoscopic images in 3DTV. Higher-level concepts such as visual experience were proposed to represent the overall visual QoE for stereoscopic images. In this paper, both the higher-level concept quality indicator, i.e. visual experience and the basic level concepts quality indicators including image quality, depth quantity, and visual comfort are defined. We aim to explore 3D QoE by constructing the visual experience as a weight sum of image quality, depth quantity and visual comfort. Two experiments in which depth quantity and image quality are varied respectively are designed to validate this model. In the first experiment, the stimuli consist of three natural scenes and for each scene, there are four levels of perceived depth variation in terms of depth of focus: 0, 0.1, 0.2 and 0.3 diopters. In the second experiment, five levels of JPEG 2000 compression ratio, 0, 50, 100, 175 and 250 are used to represent the image quality variation. Subjective quality assessments based on the SAMVIQ method are used in both experiments to evaluate the subject's opinion in basic level quality indicators as well as the higher-level indicator. Statistical analysis of result reveals how the perceived depth and image quality variation affect different perceptual scales as well as the relationship between different quality aspects

A no-reference optical flow-based quality evaluator for stereoscopic videos in curvelet domain

Most of the existing 3D video quality assessment (3D-VQA/SVQA) methods only consider spatial information by directly using an image quality evaluation method. In addition, a few take the motion information of adjacent frames into consideration. In practice, one may assume that a single data-view is unlikely to be sufficient for effectively learning the video quality. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose an effective multi-view feature learning metric for blind stereoscopic video quality assessment (BSVQA), which jointly focuses on spatial information, temporal information and inter-frame spatio-temporal information. In our study, a set of local binary patterns (LBP) statistical features extracted from a computed frame curvelet representation are used as spatial and spatio-temporal description, and the local flow statistical features based on the estimation of optical flow are used to describe the temporal distortion. Subsequently, a support vector regression (SVR) is utilized to map the feature vectors of each single view to subjective quality scores. Finally, the scores of multiple views are pooled into the final score according to their contribution rate. Experimental results demonstrate that the proposed metric significantly outperforms the existing metrics and can achieve higher consistency with subjective quality assessment