No-reference stereoscopic image quality assessment based on hue summation-difference mapping image and binocular joint mutual filtering

Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems

A high dynamic range (HDR) stereoscopic omnidirectional vision system can provide users with more realistic binocular and immersive perception, where the HDR stereoscopic omnidirectional image (HSOI) suffers distortions during its encoding and visualization, making its quality evaluation more challenging. To solve the problem, this paper proposes a client-oriented blind HSOI quality metric based on visual perception. The proposed metric mainly consists of a monocular perception module (MPM) and binocular perception module (BPM), which combine monocular/binocular, omnidirectional and HDR/tone-mapping perception. The MPM extracts features from three aspects: global color distortion, symmetric/asymmetric distortion and scene distortion. In the BPM, the binocular fusion map and binocular difference map are generated by joint image filtering. Then, brightness segmentation is performed on the binocular fusion image, and distinctive features are extracted on the segmented high/low/middle brightness regions. For the binocular difference map, natural scene statistical features are extracted by multi-coefficient derivative maps. Finally, feature screening is used to remove the redundancy between the extracted features. Experimental results on the HSOID database show that the proposed metric is generally better than the representative quality metric, and is more consistent with the subjective perception.

Multiple just-noticeable-difference-based no-reference stereoscopic image quality assessment

Just-noticeable difference (JND) is an important characteristic of the human visual system (HVS), and some established JND models imitating the perception of human eyes already exist. However, their utilization in stereoscopic image quality assessment (SIQA) remains limited. To better simulate how HVS senses 3D images under a no-reference situation, a novel SIQA method based on multiple JND models is proposed in this paper. In our metric, the stereoscopic image pairs are decomposed into multi-scale monocular views and binocular views. Then, texture and edge information of these multi-scale images is extracted. Next, a monocular JND model, a binocular JND model, and a depth JND model are separately applied to the extracted features and the depth map. Finally, these features are synthesized and mapped to objective scores. Through experiment and comparison on public 3D image databases, the proposed method shows a competitive advantage over most state-of-the-art SIQA methods, which indicates that it has a promising prospect in practical applications.