Dynamic point clouds serve as crucial representations of three-dimensional moving entities across diverse applications. The substantial amount of data in point clouds necessitates the development of efficient compression techniques. Motion estimation (ME) plays a crucial role in eliminating the temporal redundancy of point cloud sequences. However, prevailing ME methods suffer from the inaccurate geometry distortion measure and the imbalanced rate-distortion modeling, significantly impacting the coding performance. To address these challenges, we propose a rate-distortion (R-D) optimized ME scheme for dynamic point cloud geometry compression.

Learning-based lossy point cloud compression has garnered significant attention recently. However, current methods have insufficient exploration of entropy models, especially in the inter-group context. This paper proposes an innovative method called GroupAC for point cloud attribute compression by leveraging inter-group context. Initially, point cloud attributes are transformed into coefficients. We then develop a deep entropy model incorporating inter-group context to estimate the probability distribution of these coefficients. The deep entropy model splits the coefficients into groups, wherein the encoding groups utilize context from preceding groups to enhance probability distribution modeling. Finally, an arithmetic encoder compresses the coefficients into a bitstream based on the estimated probability distribution. Experimental results on indoor and outdoor point cloud datasets, including ScanNet and SemanticKITTI, demonstrate that our approach outperforms MPEG G-PCC (TMC13v23) and existing learning-based methods.

With the rapid evolution of advanced image compression, DNN-based learned image compression has emerged as the promising approach for transmitting images in many security-critical applications, such as cloud-based face recognition and autonomous driving, due to its superior performance over traditional compression. There is a pressing need to fully investigate the robustness of a classification system post-processed by learned image compression. To bridge this research gap, we explore the adversarial attack on Learned Image Compression Classification System (LICCS) that targets image classification models that utilize learned image compressors as preprocessing modules. To perform an adversarial attack on an image within the LICCS, the goal is to introduce the adversarial perturbation δ to the source image X that causes the reconstructed adversarial examples gs(Q(ga(X+δ))) to be misclassified by the classification model, which can be formulated as follows:\begin{equation*}\begin{array}{ll} {\mathop {\arg \max }\limits_i f{{\left({{g_s}\left({Q\left({{g_a}\left({{\mathbf{X + \delta }}}\right)}\right)}\right)}\right)}_i} \ne y,}&{{\text{s}}{\text{.t}}{\text{.}}\parallel \delta {\parallel _p} \leq \varepsilon .} \end{array}\tag{1}\end{equation*}

In this paper, we introduce an imperceptible adversarial attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where identifying any object in the reconstructed image is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed images from adversarial examples in order to corrupt the reconstructed image severely.

In this paper, we first extend the video-based point cloud compression (V-PCC) to support the plenoptic point cloud compression by generating multiple attribute videos. Then based on the observation that these videos from multiple views have very high correlations, we propose encoding them using multiview high efficiency video coding. We further propose a block-based padding method that unifies the unoccupied attribute pixels from different views to reduce their bit cost.

Octree (OT) geometry partitioning has been acknowledged as an efficient representation in state-of-the-art point cloud compression (PCC). In this work, a new geometry partition and coding scheme is proposed to improve the OT based coding framework, in which the quad-tree (QT) and binary-tree (BT) partitions are introduced. It brings in and harmonizes the asymmetric kd-tree like concept with the symmetric OT-based geometry coding framework. It also enables asymmetric bounding boxes that can better fit the shape of 3D scenes. Bit savings can be obtained by skipping encoding unnecessary bits from implicit QT and BT partitions. Parameters are introduced to specify the conditions on which implicit geometry partitions will be applied. Experimental results have shown significant coding gains over the OT-only coding scheme in the state-of-the-art test model of MPEG Geometry based PCC (G-PCC) standard. For the dynamically acquired point clouds, the average coding gains are 8.3% for lossy geometry coding and 4.8% for lossless geometry coding without significant increase in coding complexity.

In light of above observations, in this work, we propose a new model-based method for geometry coding in G-PCC. More specifically, it is a linear model that takes the explicit line structures as a prior knowledge to improve the coding efficiency. The encoding of the linear model can be expressed by two parts, including the principle component along the line direction and the offsets from the line. Compact representation and high-efficiency coding methods are presented by encoding the parameters of linear model with appropriate quantization step-sizes (QS). To maximize the coding performance, encoder optimization techniques are employed to find the optimal trade-off between coding bits and errors, involving the Lagrangian multiplier method, where the rate-distortion behavior in terms of QS and multiplier is analyzed. We implement our method on top of the MPEG G-PCC reference software, and the results have shown that the proposed method is effective in coding point clouds with explicit line structures, such as the Lidar acquired data for autonomous driving. About 20% coding gains can be achieved on lossy geometry coding.

Intra-picture prediction as in HEVC exploits the nearest reference line adjacent to the current coding unit (CU) for prediction of samples. If this reference line represents a discontinuity, the reference samples in this reference line can differ to a large extent from the original samples and may lead to a large prediction error. We propose a multiple reference lines (MRLs) coding to allow not only the nearest reference line 0 but also reference lines 1 and 3 to be candidates for angular intra prediction as shown in Fig. 1. To reduce the complexity arising from additional lines to be checked at encoder side, we further propose to restrict the MRL to angular most probable modes (MPMs) only. The MRL coding signals the reference line index before the intra prediction mode. This allows to not signal the MPM flag of the current CU and implicitly derive it as true when a non-zero reference line index is signaled. Experimental results are provided to evaluate the performance of the proposed MRL coding on top of the VVC test model VTM-2.0.1. 26 test sequences in different categories, including 4k, 1080p, 720p, WVGA, WQVGA resolutions and screen contents are tested. Two coding structures are evaluated, all intra (AI) and random access (RA). The objective coding efficiency is measured in terms of Bjøntegaard Delta (BD) rate (%) computed using four rate/PSNR points that were generated by using quantization parameters 22, 27, 32 and 37. Lower (negative) BD-rate implies better compression rate. Table 1 shows that the presented MRL provides 0.46% bitrate savings for an all-intra and 0.2% for a random-access configuration on average. Furthermore, it provides 1.45% bitrate reduction for screen content test sequences, which are representing an increasingly important video application. Because of a fairly good trade-off between coding efficiency and complexity, the proposed MRL coding mode with MPM restriction was adopted into the current VVC draft standard.

The Joint Video Exploration Team (JVET) recently launched the standardization of next-generation video coding named Versatile Video Coding (VVC) in which the Adaptive Multiple Transforms (AMT) is adopted as the primary residual coding transform solution. AMT introduces multiple transforms selected from the DST/DCT families and achieves noticeable coding gains. However, the set of transforms are calculated using direct matrix multiplication which induces higher run-time complexity and limits the application for practical video codec. In this paper, a fast DST-VII/DCT-VIII algorithm based on partial butterfly with dual implementation support is proposed, which aims at achieving reduced operation counts and run-time cost meanwhile yield almost the same coding performance. The proposed method has been implemented on top of the VTM-1.1 and experiments have been conducted using Common Test Conditions (CTC) to validate the efficacy. The experimental results show that the proposed methods, in the state-of-the-art codec, can provide an average of 7%, 5% and 8% overall decoding time savings under All Intra (AI), Random Access (RA) and Low Delay B (LDB) configuration, respectively yet still maintains coding performance.

This paper presents a technical overview of Wide Angular Intra Prediction (WAIP) that was adopted into the test model of Versatile Video Coding (VVC) standard. Due to the adoption of flexible block partitioning using binary and ternary splits, a Coding Unit (CU) can have either a square or a rectangular block shape. However, the conventional angular intra prediction directions, ranging from 45 degrees to -135 degrees in clockwise direction, were designed for square CUs. To better optimize the intra prediction for rectangular blocks, WAIP modes were proposed to enable intra prediction directions beyond the range of conventional intra prediction directions. For different aspect ratios of rectangular block shapes, different number of conventional angular intra prediction modes were replaced by WAIP modes. The replaced intra prediction modes are signaled using the original signaling method. Simulation results reportedly show that, with almost no impact on the run-time, on average 0.31% BD-rate reduction is achieved for intra coding using VVC test model (VTM).

Low-level image processing is mainly concerned with extracting descriptions (that are usually represented as images themselves) from images. With the rapid development of neural networks, many deep learning-based low-level image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning based approach for low-level image processing, in particular, image denoising, image deblurring, and compressed image restoration. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a multi-objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on three low-level image processing tasks on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against all current state-of-the-art approaches.

Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this article, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.

In screen content video, the spatial correlation among pixels shows different characteristics as compared to natural content video. Intra-picture motion compensation (or intra block copy in HEVC) plays a key role in reducing the bit rate of representing high resolution video with contents such as text and graphics. The efficiency of intra block copy is highly related to the accuracy of block vector prediction. In this paper, several block vector prediction methods are proposed to improve the performance of intra block copy technology in HEVC screen content coding extension. Simulation results show that an average bit rate reduction of 8.0% can be achieved for typical 1080p text and graphics sequences in all intra configuration, when compared to the standard's test model SCM-1.0. As a result, some of the proposed methods have been adopted into the standard working draft and reference software.