Video Quality Assessment Using Laplacian Modeling of Motion Vector Distribution in Mobile Video Stream

Video/Image quality assessment (VQAJIQA) is fundamental in various fields of video/image processing. VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reduced- reference mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV distributions because network resource is restricted in the case of mobile video. The proposed method is more efficient than the conventional methods in view of the computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process rather than reconstructing the distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different from the conventional methods that assess each image frame independently. For testing the similarity between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we compare the proposed method with the conventional methods with coded video clips, which are coded under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show that the proposed method gives the performance comparable with the conventional methods, especially, the proposed method requires much lower transmission data.