Video quality assessment (VQA) is becoming increasingly important as a comprehensive measure of video quality. This paper proposes a full-reference VQA (FR-VQA) algorithm based on the motion structure partition similarity of spatiotemporal slice (STS) images. To achieve this objective, a number of FR-image quality assessment algorithms were applied slice by slice to video STS images to compare their performance of detecting structure similarity of STS images. The algorithm that performed the best was selected to detect the similarity between motion-partitioning STS images. Next, as motion objects in the video sequence were found to have different influences on the prediction performance in terms of moving speed and track, the STS images were divided into simple and complex motion regions, and their contributions to the VQA task determined. Consequently, a promising effective and efficient VQA model, called STS-MSPS, is also proposed. Experimental evaluations conducted based on various annotated VQA databases indicate that the proposed STS-MSPS achieves state-of-the-art prediction performances in terms of correlations with subjective evaluation and statistical significance tests. This paper also shows that STS images by themselves provide sufficient information for VQA tasks and that the proposed complex motion region of an STS image is predominantly responsible for yielding a high-precision model.
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.
Video quality assessment (VQA) has been a hot topic due to the rapidly increasing demands in related video applications. The existing state-of-art full reference (FR) VQA metric ViS3 uses adapted the Most Apparent Distortion (MAD) algorithm to capture spatial distortion first, and then quantifies the spatiotemporal distortion by spatiotemporal correlation and a HVS-based model from the spatiotemporal slices (STS) images. In this paper we argue that the STS images can provide enough information for measuring video distortion. Taking advantage of an effective and easy-applied FR image quality model GMSD, we propose to measure video quality by analysing the structural changes between the STS images of the reference videos and their distorted counterparts. This new VQA model is denoted as STS-GMSD. To further investigate the influence spatial dissimilarity, we also combine the frame-by-frame spatial GMSD factor with the STS-GMSD and propose another VQA model, named SSTS-GMSD. Extensive experimental evaluations on two benchmark video quality databases demonstrate that the proposed STS-GMSD outperforms the existing state-of-the-art FR-VQA methods. While STS-GMSD works all square with SSTS-GMSD, which validates that STS images contain enough information for FR-VQA model design.