Browsing by Author "Jiang, Qingshan"

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    Interactive subjective study on picture-level just noticeable difference of compressed stereoscopic images
    (IEEE, 2019-05) Fan, Chunling; Zhang, Yun; Hamzaoui, Raouf; Jiang, Qingshan
    The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picturelevel JND (PJND) of compressed stereo images. The study, which involves 48 subjects and 10 stereoscopic images compressed with H.265 intra coding and JPEG2000, includes two parts. In the first part, we determine the minimum distortion that the HVS can perceive against a pristine stereo image. In the second part, we explore the minimum distortion that each subject perceives against a distorted stereo image. Modeling the distribution of the PJND samples as Gaussian, we obtain their complementary cumulative distribution functions, which are known as Satisfied User Ratio (SUR) functions. Statistical analysis results demonstrate that the SUR is highly dependent on the image contents. The HVS is more sensitive to distortion in images with more texture details. The compressed stereoscopic images and the PJND samples are collected in a data set called SIAT-JSSI, which we release to the public.
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    Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images
    (IEEE, 2021) Fan, Chunling; Zhang, Yun; Hamzaoui, Raouf; Ziou, Djemel; Jiang, Qingshan
    The Satisfied User Ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for 2D images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images.
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    Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereoscopic images: Subjective quality assessment study and datasets
    (Elsevier, 2019-05-10) Fan, Chunling; Zhang, Yun; Zhang, Huan; Hamzaoui, Raouf; Jiang, Qingshan
    The Picture-level Just Noticeable Difference (PJND) for a given image and compression scheme reflects the smallest distortion level that can be perceived by an observer with respect to a reference image. Previous work has focused on the PJND of images and videos. In this paper, we study the PJND of symmetrically and asymmetrically compressed stereoscopic images for JPEG2000 and H.265 intra coding. We conduct interactive subjective quality assessment tests to determine the PJND point using both a pristine image and a distorted image as a reference. We find that the PJND points are highly dependent on the image content. In asymmetric compression, there exists a perceptual threshold in the quality difference between the left and right views due to the binocular masking effect. We generate two PJND-based stereo image datasets (one for symmetric compression and one for asymmetric compression) and make them accessible to the public.
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    Satisfied user ratio prediction with support vector regression for compressed stereo images
    (IEEE, 2020-07) Fan, Chunling; Zhang, Yun; Hamzaoui, Raouf; Ziou, Djemel; Jiang, Qingshan
    We propose the first method to predict the Satisfied User Ratio (SUR) for compressed stereo images. The method consists of two main steps. First, considering binocular vision properties, we extract three types of features from stereo images: image quality features, monocular visual features, and binocular visual features. Then, we train a Support Vector Regression (SVR) model to learn a mapping function from the feature space to the SUR values. Experimental results on the SIAT-JSSI dataset show excellent prediction accuracy, with a mean absolute SUR error of only 0.08 for H.265 intra coding and only 0.13 for JPEG2000 compression.
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    SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning
    (IEEE, 2019-06) Fan, Chunling; Lin, Hanhe; Hosu, Vlad; Zhang, Yun; Jiang, Qingshan; Hamzaoui, Raouf; Saupe, Dietmar
    The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.