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.

Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression

Machine learning based video coding optimizations: A survey

Virtual reality (VR), a new type of simulation and interaction technology, has aroused widespread attention and research interest. It is necessary to evaluate the VR quality and provide a standard for the rapidly developing technology. To the best of our knowledge, a few researchers have built benchmark databases and designed related algorithms, which has hindered the further development of the VR technology. In this paper, a free available data set (VRQ-TJU) for VR quality assessment is proposed with subjective scores for each sample data. The validity for the designed database has been proved based on the traditional multimedia quality assessment metrics. In addition, an end-to-end 3-D convolutional neural network is introduced to predict the VR video quality without a referenced VR video. This method can extract spatiotemporal features and does not require using hand-crafted features. At the same time, a new score fusion strategy is designed based on the characteristics of the VR video projection process. Taking the pre-processed VR video patches as input, the network captures local spatiotemporal features and gets the score of every patch. Then, the new quality score fusion strategy is applied to get the final score. Such approach shows advanced performance on this database.

3D Panoramic Virtual Reality Video Quality Assessment Based on 3D Convolutional Neural Networks

Blind binocular visual quality prediction (BVQP) is more challenging than blind monocular visual quality prediction (MVQP). Recently, the application of convolutional neural networks (CNNs) to blind MVQP has resulted in significant progress in that area. In contrast, the adoption of deep learning for blind BVQP has received scant attention. In this study, we devised an end-to-end deep fusion network (DFNet) model trained in a unified framework for blind BVQP. This core prediction engine comprises monocular feature encoding networks and binocular feature fusion networks, followed by a quality prediction layer. The monocular feature encoding networks are first established to capture the low- and high-level monocular features of the left and right retinal views, respectively. Subsequently, these monocular features are integrated by the binocular feature fusion networks to obtain binocular deep features. Finally, the final binocular visual quality is predicted by quality prediction networks. Comparisons via experiments using two standard subject-rated BVQP datasets indicate that the proposed DFNet architecture achieves highly consistent alignment with human assessment and outperforms most relevant existing models.

Blind Binocular Visual Quality Predictor Using Deep Fusion Network

In this paper, we propose an effective approach to infer the just noticeable distortion (JND) profile based on patch-level structural visibility learning. Instead of pixel-level JND profile estimation, the image patch, which is regarded as the basic processing unit to better correlate with the human perception, can be further decomposed into three conceptually independent components for visibility estimation. In particular, to incorporate the structural degradation into the patch-level JND model, a deep learning-based structural degradation estimation model is trained to approximate the masking of structural visibility. In order to facilitate the learning process, a JND dataset is further established, including 202 pristine images and 7878 distorted images generated by advanced compression algorithms based on the upcoming Versatile Video Coding (VVC) standard. Extensive experimental results further show the superiority of the proposed approach over the state-of-the-art. Our dataset is available at: https://github.com/ShenXuelin-CityU/PWJNDInfer .

Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning Approach

No Reference (NR) Image Quality Assessment (IQA) algorithm is capable of measuring the quality of distorted images without referencing the original images. This property is of great importance in image processing, compression, and transmission. However, due to the diversity of the distortion types and image contents, it is difficult for the existing NR IQA algorithms to be applied and maintain the best performance for all cases. To address this problem, we develop a novel NR IQA algorithm based on multi-expert convolutional neural networks (CNNs), which consists of distortion type classification, CNN based IQA algorithms and fusion algorithm. First, we present a distortion type classifier to identify the distortion type of the input image. Then, we propose a multi-expert CNN based IQA algorithms for each type of distortion. Finally, a fusion algorithm is adopted to aggregate the classification result of distortion types and multi-expert CNN based image quality predictions. The proposed algorithm has been tested on commonly used LIVE II database and a cross-dataset evaluation was carried on CSIQ database. The experimental results show that the proposed algorithm provides effective improvements for NR IQA.

No Reference Image Quality Assessment based on Multi-Expert Convolutional Neural Networks

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset1 has been released in the public repository.

Subjective Quality Database and Objective Study of Compressed Point Clouds with 6DoF Head-mounted Display

Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereoscopic images: Subjective quality assessment study and datasets

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.

/pdf/sur-net-predicting-the-satisfied-user-ratio-curve-for-image-1k9nbltcm8.pdf

Chunling Fan

Papers

Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression

No Reference Image Quality Assessment based on Multi-Expert Convolutional Neural Networks

Subjective Quality Database and Objective Study of Compressed Point Clouds with 6DoF Head-mounted Display

Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereoscopic images: Subjective quality assessment study and datasets

SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning