Multiscale-SSIM Index Based Stereoscopic Image Quality Assessment

Stereoscopic image quality typically depends on two factors: i) the quality of the luminance image perception, and ii) the quality of depth perception. The effect of distortion on luminance perception and depth perception is usually different, even though depth is estimated from luminance images. Therefore, we present a full reference stereoscopic image quality assessment (FRSIQA) algorithm that rates stereoscopic images in proportion to the quality of individual luminance image perception and the quality of depth perception. The luminance and depth quality is obtained by applying the robust Multiscale-SSIM (MS-SSIM) index on both luminance and disparity maps respectively. We propose a novel multi-scale approach for combining the luminance and depth scores from the left and right images into a single quality score per stereo image. We also explained that a small amount of distortion does not significantly affect depth perception. Further, heavy distortion in stereo pairs will result in significant loss of depth perception. Our algorithm performs competitively over standard databases and is called the 3D-MS-SSIM index

Stereoscopic 3D image quality assessment based on cyclopean view and depth map

International audienceThis paper presents a full reference quality assessment metric for stereoscopic images based on perceptual binocular characteristics. To ensure that the predicted 3D quality of experience is as reliable and close as possible to 3D human perception, the proposed stereoscopic image quality assessment (SIQA) method is relying on the cyclopean image. Our approach is motivated by the fact that in case of asymmetric quality, 3D perception mechanisms place more emphasis on the view providing the most important and contrasted information. We integrated this psychophysical findings in the proposed 3D-IQA framework thanks to a weighting factor based on local information content. Add to that, to take into account the disparity/depth masking effect, we modulate the obtained quality score of each pixel of the cyclopean image according to its location in the scene. Experimental results show that the proposed metric correlates better with human judgement than the state-of-the-art metrics