Research Article Visual Perception Based Objective Stereo Image Quality Assessment for 3D Video Communication

Abstract: Stereo image quality assessment is a crucial and challenging issue in 3D video communication. One of major difficulties is how to weigh binocular masking effect. In order to establish the assessment mode more in line with the human visual system, Watson model is adopted, which defines visibility threshold under no distortion composed of contrast sensitivity, masking effect and error in this study. As a result, we propose an Objective Stereo Image Quality Assessment method (OSIQA), organically combining a new Left-Right view Image Quality Assessment (LR-IQA) metric and Depth Perception Image Quality Assessment (DP-IQA) metric. The new LR-IQA metric is first given to calculate the changes of perception coefficients in each sub-band utilizing Watson model and human visual system after wavelet decomposition of left and right images in stereo image pair, respectively. Then, a concept of absolute difference map is defined to describe abstract differential value between the left and right view images and the DP-IQA metric is presented to measure structure distortion of the original and distorted abstract difference maps through luminance function, error sensitivity and contrast function. Finally, an OSIQA metric is generated by using multiplicative fitting of the LR-IQA and DP-IQA metrics based on weighting. Experimental results shows that the proposed method are highly correlated with human visual judgments (Mean Opinion Score) and the correlation coefficient and monotony are more than 0.92 under five types of distortions such as Gaussian blur, Gaussian noise, JP2K compression, JPEG compression and H.264 compression

Objective No-Reference Stereoscopic Image Quality Prediction Based on 2D Image Features and Relative Disparity

Stereoscopic images are widely used to enhance the viewing experience of three-dimensional (3D) imaging and communication system. In this paper, we propose an image feature and disparity dependent quality evaluation metric, which incorporates human visible system characteristics. We believe perceived distortions and disparity of any stereoscopic image are strongly dependent on local features, such as edge (i.e., nonplane areas of an image) and nonedge (i.e., plane areas of an image) areas within the image. Therefore, a no-reference perceptual quality assessment method is developed for JPEG coded stereoscopic images based on segmented local features of distortions and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness and the edge distortion within the block of images are evaluated in this method. Subjective stereo image database is used for evaluation of the metric. The subjective experiment results indicate that our metric has sufficient prediction performance