Video Quality Assessment Based On The Structural Similarity And Its Application On Video Communication

Recent advances in multimedia communication technology have resulted in the proliferation of digital images, both still and video. However, digital image and video are subject to a wide variety of distortions during acquisition, processing, compression, storage, transmission and reproduction, so it's always need to measurement its quality. In practice, however, subjective evaluation is usually too inconvenient, time-consuming and expensive. The traditional objective methods such as MSE and PSNR have low correlation with the perceptual visual quality. So it's necessary to develop new objective methods which can correspond better to subjective feelings.The philosophy of the structural similarity based on the assumption that the human visual system is highly adapted to extract structural information from the viewing field. It follows that a measure of structural information change can provide a good approximation to perceived image distortion. This paper studies the applications of the Structural SIMilarity(SSIM)index on image and video quality assessment, and develop its applications on the video communication.Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. On the other hand, the human visual system is very sensitive to the low quality of the video detected. We seek to ameliorate this by developing a new VQA algorithm based on the structural similarity. Video quality is evaluated not only in space, but also in time. It is found that the stVSSIM index delivers VQA scores that correlate quite closely with human subjective judgment.On the other hand, we propose a novel H.264 fast motion estimation algorithm based on the structural similarity, the characteristics of SSIM are considered, a SSIM threshold is set for the motion estimation process and the unnecessary searching positions are eliminated, thus reducing the complexity of motion estimation and improving the coding performance. It is found that the FMESS effectively saves the coding time and improves the compression ratio without reducing the video quality.