Full-Reference Stereoscopic Video Quality Assessment Using a Motion Sensitive HVS Model

Abstract

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics.

No-Reference Quality Assessment of Stereoscopic Video Based on Temporal Adaptive Model for Improved Visual Communication

“…The Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SROCC) and root-mean-square error (RMSE) were used to measure the relationship between the objective prediction results and subjective evaluation scores, and then to verify the effectiveness of this method. The prediction accuracy and monotonicity of the prediction sample [ 25 ] were measured by the PLCC and SROCC, respectively. Higher values indicated a better performance of this method.…”

Reduced-Reference Stereoscopic Image Quality Assessment Using Gradient Sparse Representation and Structural Degradation

“…In [47], Liu et al [46] proposed a novel FR SIQA metric by considering the depth information and integral color information of 3D image under cloud computing environment. Galkandage et al [48] designed a stereoscopic video quality index based on the motion sensitive HVS model. However, because of the complexity of HVS is unsurpassed in nature as we know, the above-mentioned SIQA models may not precisely response the change caused by different distortions.…”

Video Quality Assessment based on Deep Neural Network

“…Finally, the visual memory, saliency and frame features were trained by support vector regression machine to obtain video quality [6]. Chathura Galkandage designed a novel visual perception assessment model based on the motion sensitive response of complex cells in the visual cortex, used to simulate the characteristics of simple and complex cell behavior, and finally determined the weight of each feature through the customized double order multiple stepwise regression algorithm [7]. These models based on visual psychology don't need complex training process and calculation process, but it mainly studies the function model of video quality and some visual features, and can't solve the problem of multiple video features synthesis, which also brings some limitations to the application of this kind of model.…”