The Experts below are selected from a list of 3171 Experts worldwide ranked by ideXlab platform
Yun Zhang - One of the best experts on this subject based on the ideXlab platform.
-
deep learning based picture wise Just Noticeable distortion prediction model for image compression
IEEE Transactions on Image Processing, 2020Co-Authors: Huanhua Liu, Yun Zhang, Huan Zhang, Chunling Fan, Sam Kwong, C Jay C Kuo, Xiaoping FanAbstract:Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum Difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by Just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models.
-
sur net predicting the satisfied user ratio curve for image compression with deep learning
Quality of Multimedia Experience, 2019Co-Authors: Vlad Hosu, Qingshan Jiang, Raouf Hamzaoui, Yun Zhang, Dietmar SaupeAbstract: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.
-
Interactive Subjective Study on Picture-level Just Noticeable Difference of Compressed Stereoscopic Images
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Yun Zhang, Raouf Hamzaoui, Qingshan JiangAbstract: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 Picture-level 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.
Wang Yunjin - One of the best experts on this subject based on the ideXlab platform.
-
enhancing iris feature security with steganography
Conference on Industrial Electronics and Applications, 2010Co-Authors: Wang Na, Zhang Chiya, Wang YunjinAbstract:This paper introduces a novel steganography-based approach to protect the iris data by hiding it into a digital image for personal identification purpose. Logistic map is utilized for generating two pseudorandom sequences, one for encrypting the biometric data before hiding, and another for hiding position encryption. Meanwhile, JND (Just Noticeable Difference) model and JPEG quantization table are investigated for exploring an efficient hiding algorithm with high transparency and strong robustness especially for JPEG compression. The extraction of iris data does not require original image and provide high accuracy under different attacks. Experimental results show that this proposed method improves the security of the iris-feature with hardly detectable decrease in recognition performance.
-
enhancing iris feature security with steganography
Conference on Industrial Electronics and Applications, 2010Co-Authors: Wang Na, Zhang Chiya, Wang YunjinAbstract:This paper introduces a novel steganography-based approach to protect the iris data by hiding it into a digital image for personal identification purpose. Logistic map is utilized for generating two pseudorandom sequences, one for encrypting the biometric data before hiding, and another for hiding position encryption. Meanwhile, JND (Just Noticeable Difference) model and JPEG quantization table are investigated for exploring an efficient hiding algorithm with high transparency and strong robustness especially for JPEG compression. The extraction of iris data does not require original image and provide high accuracy under different attacks. Experimental results show that this proposed method improves the security of the iris-feature with hardly detectable decrease in recognition performance.
Qingshan Jiang - One of the best experts on this subject based on the ideXlab platform.
-
sur net predicting the satisfied user ratio curve for image compression with deep learning
Quality of Multimedia Experience, 2019Co-Authors: Vlad Hosu, Qingshan Jiang, Raouf Hamzaoui, Yun Zhang, Dietmar SaupeAbstract: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.
-
Interactive Subjective Study on Picture-level Just Noticeable Difference of Compressed Stereoscopic Images
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Yun Zhang, Raouf Hamzaoui, Qingshan JiangAbstract: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 Picture-level 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.
Wang Na - One of the best experts on this subject based on the ideXlab platform.
-
enhancing iris feature security with steganography
Conference on Industrial Electronics and Applications, 2010Co-Authors: Wang Na, Zhang Chiya, Wang YunjinAbstract:This paper introduces a novel steganography-based approach to protect the iris data by hiding it into a digital image for personal identification purpose. Logistic map is utilized for generating two pseudorandom sequences, one for encrypting the biometric data before hiding, and another for hiding position encryption. Meanwhile, JND (Just Noticeable Difference) model and JPEG quantization table are investigated for exploring an efficient hiding algorithm with high transparency and strong robustness especially for JPEG compression. The extraction of iris data does not require original image and provide high accuracy under different attacks. Experimental results show that this proposed method improves the security of the iris-feature with hardly detectable decrease in recognition performance.
-
enhancing iris feature security with steganography
Conference on Industrial Electronics and Applications, 2010Co-Authors: Wang Na, Zhang Chiya, Wang YunjinAbstract:This paper introduces a novel steganography-based approach to protect the iris data by hiding it into a digital image for personal identification purpose. Logistic map is utilized for generating two pseudorandom sequences, one for encrypting the biometric data before hiding, and another for hiding position encryption. Meanwhile, JND (Just Noticeable Difference) model and JPEG quantization table are investigated for exploring an efficient hiding algorithm with high transparency and strong robustness especially for JPEG compression. The extraction of iris data does not require original image and provide high accuracy under different attacks. Experimental results show that this proposed method improves the security of the iris-feature with hardly detectable decrease in recognition performance.
Raouf Hamzaoui - One of the best experts on this subject based on the ideXlab platform.
-
sur net predicting the satisfied user ratio curve for image compression with deep learning
Quality of Multimedia Experience, 2019Co-Authors: Vlad Hosu, Qingshan Jiang, Raouf Hamzaoui, Yun Zhang, Dietmar SaupeAbstract: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.
-
Interactive Subjective Study on Picture-level Just Noticeable Difference of Compressed Stereoscopic Images
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Yun Zhang, Raouf Hamzaoui, Qingshan JiangAbstract: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 Picture-level 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.
