Just Noticeable Difference Model for Asymmetrically Distorted Stereoscopic Images

Abstract

In this paper, we propose a saliency-weighted stereoscopic JND (SSJND) model constructed based on psychophysical experiments, accounting for binocular disparity and spatial masking effects of the human visual system (HVS). Specifically, a disparity-aware binocular JND model is first developed using psychophysical data, and then is employed to estimate the JND threshold for non-occluded pixel (NOP). In addition, to derive a reliable 3D-JND prediction, we determine the visibility threshold for occluded pixel (OP) by including a robust 2D-JND model. Finally, SSJND thresholds of one view are obtained by weighting the resulting JND for NOP and OP with their visual saliency. Based on subjective experiments, we demonstrate that the proposed model outperforms the other 3D-JND models in terms of perceptual quality at the same noise level.

Quality Assessment for DIBR-Synthesized Images With Local and Global Distortions

“…Yue et al [7] proposed an effective blind quality assessment approach for TM images through a comprehensive consideration of their characteristics. Fan et al [8] integrated 3D-JND models in stereoscopic image quality assessment. Jiang et al [9] propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs) to address the memory-consuming, over-fitting and semantic gap problems.…”