Showing papers by "Mohamed-Chaker Larabi published in 2019"

A Survey of Stereoscopic 3D Just Noticeable Difference Models

Abstract: Just noticeable difference (JND) for stereoscopic 3D content reflects the maximum tolerable distortion; it corresponds to the visibility threshold of the asymmetric distortions in the left and right contents. The 3D-JND models can be used to improve the efficiency of the 3D compression or the 3D quality assessment. Compared to 2D-JND models, the 3D-JND models appeared recently and the related literature is rather limited. In this paper, we give a deep and comprehensive study of the pixel-based 3D-JND models. To our best knowledge, this is the first review on 3D-JND models. Each model is briefly described by giving its rationale and main components in addition to providing exhaustive information about the targeted application, the pros, and cons. Moreover, we present the characteristics of the human visual system presented in these models. In addition, we analyze and compare the 3D-JND models thoroughly using qualitative and quantitative performance evaluation based on Middlebury stereo datasets. Besides, we measure the JND thresholds of the asymmetric distortion based on psychophysical experiments and compare these experimental results to the estimates from the 3D-JND models in order to evaluate the accuracy of each model.

Blind Stereopair Quality Assessment Using Statistics of Monocular and Binocular Image Structures

Abstract: In this paper, we present a no-reference (NR) quality predictor for stereoscopic/3D images based on statistics aggregation of monocular and binocular local contrast features. In particular, for left and right views, we first extract statistical features of the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG), describing the image local structures from different perspectives. The monocular statistical features are then combined to derive the binocular features based on a linear summation model using weightings based on LoGresponse and image local-entropy, independently. These weights can effectively simulate the strength of the views dominance on binocular rivalry (BR) behavior of the human visual system. Subsequently, we further compute the GM features of the difference map between left and right views reflecting the distortion on disparity/depth information. Finally, the BR-inspired combined monocular and disparityrelated binocular features associated with subjective quality scores are jointly used to construct a learned regression model relying on support vector machine regressor. Experimental results on three 3D-IQA benchmark databases demonstrate that our method achieves high quality prediction accuracy and competitive performance compared to state-of-the-art methods.

Fast Object Detection in H264/AVC and HEVC Compressed Domains for Video Surveillance

Abstract: In this paper, we propose a new algorithm for fast object detection in the compressed domain of H.264/AVC and HEVC video coding standards. The proposed algorithm is based on three coding parameters: motions vectors, block types and transform coefficients which are extracted from the partially decoded video bitstream. Each feature is separately processed to remove noise and to empowers its discrimination ability. The obtained feature maps are segmented using a fuzzy clustering algorithm. Finally, a fusion step allows merging the segmented feature maps thanks to a weighted linear combination. The originality of this approach lies in its application to both H.264/AVC and HEVC compressed domains. Experiments are conducted using ten test sequences obtained in order to evaluate the performance of the proposed approach. The proposed method has been compared with a state-of-the-art pixel-domain algorithm in term of F-score and computing time.

Flexible Motion Vector Resolution Prediction for Video Coding

Abstract: The latest video coding standard, High Efficiency Video Coding (HEVC), uses quarter-pixel motion vector (MV) resolution for motion compensation. The adaptation of MV resolution supported by progressive MV resolution (PMVR) brings further improvement to performance by progressively adjusting the resolution according to the distance between the MV and its predictor. However, progressive adjustment of resolution by PMVR does not consider the inherent characteristics of the coding block. In this paper, we propose several ways to improve PMVR. First, we show that the performance of PMVR is correlated with the spatiotemporal characteristics of the video sequence. Then, to cope with the limitations of PMVR, we propose a flexible framework for the adaptation of MV resolution using: 1) PU size and gradient; 2) PU size, gradient, and MV components; and 3) PU size and spatiotemporal characteristics of the frames. Finally, a smart motion estimation around multiple MV predictors is performed to take full advantage of the proposed scheme. The proposed tools are implemented on top of HM-16.6. Extensive experiments and comparison with HEVC show 1.3%, 2.7%, and 1.0% average BD-Rate savings for random access, low-delay P, and low-delay B configurations, respectively.

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.