Video quality assessment based on correlation between spatiotemporal motion energies

Peng Yan; Xuanqin Mou

doi:10.1117/12.2236454

28 September 2016 Video quality assessment based on correlation between spatiotemporal motion energies

Peng Yan, Xuanqin Mou

Proceedings Volume 9971, Applications of Digital Image Processing XXXIX; 997130 (2016) https://doi.org/10.1117/12.2236454
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

Video quality assessment (VQA) has been a hot research topic because of rapid increase of huge demand of video communications. From the earliest PSNR metric to advanced models that are perceptual aware, researchers have made great progress in this field by introducing properties of human vision system (HVS) into VQA model design. Among various algorithms that model the property of HVS perceiving motion, the spatiotemporal energy model has been validated to be high consistent with psychophysical experiments. In this paper, we take the spatiotemporal energy model into VQA model design by the following steps. 1) According to the pristine spatiotemporal energy model proposed by Adelson et al, we apply the linear filters, which are oriented in space-time and tuned in spatial frequency, to filter the reference and test videos respectively. The outputs of quadrature pairs of above filters are then squared and summed to give two measures of motion energy, which are named rightward and leftward energy responses, respectively. 2) Based on the pristine model, we calculate summation of the rightward and leftward energy responses as spatiotemporal features to represent perceptual quality information for videos, named total spatiotemporal motion energy maps. 3) The proposed FR-VQA model, named STME, is calculated with statistics based on the pixel-wise correlation between the total spatiotemporal motion energy maps of the reference and distorted videos. The STME model was validated on the LIVE VQA Database by comparing with existing FR-VQA models. Experimental results show that STME performs with excellent prediction accuracy and stays in state-of-the-art VQA models.

Citation Download Citation

Peng Yan and Xuanqin Mou "Video quality assessment based on correlation between spatiotemporal motion energies", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997130 (28 September 2016); https://doi.org/10.1117/12.2236454

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