The Picture-Wise Just Noticeable Difference (PW-JND) represents the visibility threshold of human vision when viewing distorted images. The PW-JND plays an important role in perceptual image processing and compression. However, predicting the PW-JND is challenging due to its dependence on image content, viewing conditions, and the viewer. In this paper, we propose a visual perception-assisted deep PW-JND (VP-JND) prediction model for image compression that combines data-driven methods with the perceptual mechanisms of human vision. First, we identify a correlation between PW-JND and conventional pixel-wise JND. Based on this observation, we design the VP-JND model, consisting of a pixel-wise JND model, a deep binary classifier (VP-JNDnet) and a binary block search algorithm for refining predictions. VP-JNDnet exploits the pixel-wise JND map of the original image to predict whether a compressed image is perceptually lossless. In addition, the model incorporates visual importance of content and regions by using a mixed attention module and calculating perceptual loss during training. Experimental results show that VP-JND achieved an average precision of 94.82% and a mean absolute difference of 3.92 in predicting the JPEG quality factor corresponding to the PW-JND on the MCL-JCI dataset, outperforming state-of-the-art JND models. When applied to perceptual lossless image coding, the predicted PW-JND enabled average bit rate savings of 89.35% for JPEG compression on MCL-JCI and 85.46%/41.13% for JPEG/BPG compression on KonJND-1k. These savings were relative to images compressed at the lowest distortion level. The source codes and trained models are publicly available at https://github.com/SYSU-Video/VP-JND.

Point Cloud Quality Assessment (PCQA) aims to accurately predict the visual quality of a point cloud, which is essential in optimizing and evaluating the point cloud compression, transmission and rendering. In this paper, we propose a deep learning based full reference PCQA using 3D-to-2D Regularized Representation (RegR-PCQA), where point clouds are projected to regularized 2D image representations and then measured with deep neural networks. Firstly, we propose a regularized representation module to project unstructured point clouds to 2D Regularized Geometry Images (RGIs) and Regularized Attribute Images (RAIs), which enhance the local adjacency and uniform distribution of points. An anchor matching is developed to build the correspondence of regularized images between the distorted and reference point clouds. Secondly, to exploit the visual features of the RGIs and RAIs, we propose a deep learning based two-branch PCQA network, in which vision transformer based Geometry Feature Extractor (GFE) extracts global structural features from RGIs and Convolutional Neural Network (CNN) based Attribute Feature Extractor (AFE) extracts local semantic features of the RAIs. Finally, based on the geometry and attribute features, the point cloud quality is predicted by the proposed quality regression module, where a spatial attention mechanism is exploited to assign different importance weights for the feature maps. Experimental results show that the Pearson Linear Correlation Coefficients (PLCC) achieved by the proposed RegR-PCQA are 0.8430, 0.9575, 0.7853 and 0.8576, respectively, on the SIAT-PCQD, SJTU-PCQA, WPC and WPC2.0 datasets, which are superior to the state-of-the-art PCQAs. Also, extensive experimental results on distortion types, sampling strategy and training rate show that the proposed RegR-PCQA achieves an excellent generalization.

Point Cloud Quality Assessment (PCQA) plays an essential role in optimizing point cloud acquisition, encoding, transmission, and rendering for human-centric visual media applications. In this paper, we propose an objective PCQA model using Complementary Features from 3D and 2D spaces, called CF-PCQA, to measure the visual quality of colored point clouds. First, we develop four effective features in 3D space to represent the perceptual properties of colored point clouds, which include curvature, kurtosis, luminance distance and hue features of points in 3D space. Second, we project the 3D point cloud onto 2D planes using patch projection and extract a structural similarity feature of the projected 2D images in the spatial domain, as well as a sub-band similarity feature in the wavelet domain. Finally, we propose a feature selection and a learning model to fuse high dimensional features and predict the visual quality of the colored point clouds. Extensive experimental results show that the Pearson Linear Correlation Coefficients (PLCCs) of the proposed CF-PCQA were 0.9117, 0.9005, 0.9340 and 0.9826 on the SIAT-PCQD, SJTU-PCQA, WPC2.0 and ICIP2020 datasets, respectively. Moreover, statistical significance tests demonstrate that the CF-PCQA significantly outperforms the state-of-the-art PCQA benchmark schemes on the four datasets.

Abstract Deep‐sea image is of great significance for exploring seabed resources. However, the information of a single image is limited. Besides, deep‐sea image with low contrast and colour distortion further restricts useful feature extraction. To address the issues above, this paper presents a multi‐channel fusion and accelerated‐KAZE (AKAZE) feature detection algorithm for deep‐sea image stitching. First, the authors restore deep‐sea image in LAB colour space and RGB colour space, respectively; in LAB space, the authors use homomorphic filtering in L colour channel, and in RGB space, the authors adopt multi‐scale Retinex with chromaticity preservation algorithm to adjust the colour information. Then, the authors blend two pre‐processed images with dark channel prior weighted coefficient. After that, the authors detect feature points with the AKAZE algorithm and obtain feature descriptors with Boosted Efficient Binary Local Image Descriptor. Finally, the authors match the feature points and warp deep‐sea images to obtain the stitched image. Experimental results demonstrate that the authors’ method generates high‐quality stitched image with minimized seam. Compared with state‐of‐the‐art algorithms, the proposed method has better quantitative evaluation, visual stitching results, and robustness.

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 datasethttps://dx.doi.org/10.21227/ad8d-7r28http://codec.siat.ac.cn/video_download_siat-pcqd.htmlhasbeen released in the public repository.

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 shows the superiority of the proposed PW-JND model to the conventional JND models.

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.

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.

The idea of multisensor integration is to use multiple sensors for measuring the same variables, where each sensor has its own accuracy, reliability and drawbacks. The sensor information is integrated by some data integration algorithms. In this paper, Radial Basis Function (RBF) neural networks and multisensor data fusion technology are combined and used in the fault detection and diagnosis of sensors hardware faults in the Satellite Attitude Measurement System (SAMS). The fusion method of the RBF neural networks is adopted. By using the combination method the outputs of the system are more accurate and reliable than each individual sensor. Research results show that this method for the detection and diagnosis of the sensors hardware faults in the SAMS is feasible and more effective, and for the sensors which measure the same attitude angle, using the method of firstly integration, then faults diagnosis, finally connection with the measurement system, the systematic measurement precision and performance-price-ratio can be improved.

The attitude control of a future satellite is facing the challenge of its uncertain model because of flexural bodies coupling to the center body. This paper proposes a neuro-fuzzy approach to control the uncertain dynamics and disturbance. The flexible model of solar array is extended to the satellite attitude equation. To overcome the variation of internal parameters and external disturbances, three adaptive neuro-fuzzy controllers are applied to realize the attitude and orbit control system (AOCS) on the base of Proportional-Integral-Differential (PID) controllers. A least square estimation method is combined with gradient descent learning in order to optimize the learning error universally during train procedure. Simulation results show the neuro-fuzzy controllers have good performance to abate influence from disturbance, fadeless vibration and the change of the satellite's inertia parameters.