Dendi Sathya Veera Reddy, Channappayya Sumohana S
IEEE Trans Image Process. 2020 Apr 7. doi: 10.1109/TIP.2020.2984879.
Robust spatiotemporal representations of natural videos have several applications including quality assessment, action recognition, object tracking etc. In this paper, we propose a video representation that is based on a parameterized statistical model for the spatiotemporal statistics of mean subtracted and contrast normalized (MSCN) coefficients of natural videos. Specifically, we propose an asymmetric generalized Gaussian distribution (AGGD) to model the statistics of MSCN coefficients of natural videos and their spatiotemporal Gabor bandpass filtered outputs. We then demonstrate that the AGGD model parameters serve as good representative features for distortion discrimination. Based on this observation, we propose a supervised learning approach using support vector regression (SVR) to address the no-reference video quality assessment (NRVQA) problem. The performance of the proposed algorithm is evaluated on publicly available video quality assessment (VQA) datasets with both traditional and in-capture/authentic distortions. We show that the proposed algorithm delivers competitive performance on traditional (synthetic) distortions and acceptable performance on authentic distortions. The code for our algorithm will be released at https://www.iith.ac.in/~lfovia/downloads.html.
自然视频强大的时空表征有多种应用，包括质量评估、动作识别、目标跟踪等。在本文中，我们提出一种视频表征，它基于一个参数化统计模型，用于对自然视频减去均值并归一化对比度（MSCN）系数的时空统计特性进行建模。具体而言，我们提出一种非对称广义高斯分布（AGGD）来对自然视频的MSCN系数及其时空Gabor带通滤波输出的统计特性进行建模。然后我们证明AGGD模型参数可作为失真判别良好的代表性特征。基于这一观察结果，我们提出一种使用支持向量回归（SVR）的监督学习方法来解决无参考视频质量评估（NRVQA）问题。在具有传统失真和拍摄中/真实失真的公开可用视频质量评估（VQA）数据集上对所提算法的性能进行了评估。我们表明，所提算法在传统（合成）失真方面具有有竞争力的性能，在真实失真方面具有可接受的性能。我们算法的代码将在https://www.iith.ac.in/~lfovia/downloads.html上发布。
