Video quality assessment by compact representation of energy in 3D-DCT domain
Video quality assessment by compact representation of energy in 3D-DCT domain
复制标题
通过 3D-DCT 域中能量的紧凑表示来评估视频质量
DOI：
10.1016/j.neucom.2016.08.143
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发表时间：
2017-12-20
期刊：
影响因子：
6
通讯作者：
Hao, Lei
中科院分区：
文献类型：
--
作者：
He, Lihuo;Lu, Wen;Hao, Lei
视频质量评估(VQA)旨在预测实际应用系统的感知质量，以提高系统的性能。然而，传统的方法将视频看作是一个二维图像序列，这与视频信号是三维体数据的事实相冲突。这种操作忽略了时间信息，导致与人类感知的一致性较差。因此，通过探索和利用三维离散余弦变换(3D-DCT)域中能量的紧致表示，提出了一种新的VQA模型。首先，对每组帧(GOF)进行3D-DCT变换。然后从3D-DCT系数中提取三种统计特征来表示能量压缩特性，以模拟人类视觉系统(HVS)的过程。通过估计广义高斯分布(GGD)的参数来模拟3D-DCT系数的边缘分布。计算了三个能量比来描述视频能量在不同频率分量上的分布情况。并利用3D-DCT系数的绝对均值和方差来度量视频的频率变化。最后，计算参考视频特征与失真视频特征之间的差值，预测失真视频的质量分数。实验结果表明，本文提出的VQA方法与人的感知具有较好的一致性，与目前最先进的VQA方法相比具有较强的竞争力。(C)2017爱思唯尔B.V.保留所有权利。
Video quality assessment (VQA) aims to pfedict the perceptual quality for improving the performance of practical application systems. However, the traditional methods consider the video as a sequence of two-dimensional images, which conflicts with the fact that a video signal is a three-dimensional volume data. This operation ignores the temporal information and results in a poor consistency with human perception. Hence, the paper presents a novel VQA model by exploring and exploiting the compact representation of energy in the three-dimensional discrete cosine transform (3D-DCT) domain. First, the video is transformed by 3D-DCT for every group of frame (GOF). Then three types of statistical features are derived from the 3D-DCT coefficients to represent energy compaction properties for simulating the process of human visual system (HVS). The parameters of the generalized Gaussian distribution (GGD) are estimated to imitate the marginal distribution of the 3D-DCT coefficients. Three energy ratios are calculated to depict how the video energy distributes over different frequency components. And the mean and variance value of absolute 3D-DCT coefficients are employed to measure the frequency variation of the video. Finally, the differences between the feature of reference video and the feature of distorted video are calculated to predict the quality score of the distorted video. Experimental results show that the proposed VQA method has a good consistency with human perception and is competitive with the state-of-the-art methods. (C) 2017 Elsevier B.V. All rights reserved.