Video-based Point Cloud Compression Research Articles

V-PCC Projection Based Blind Point Cloud Quality Assessment for Compression Distortion

In immersive media communication with point cloud (PC), PC compression will inevitably cause distortion, which may affect quality of users’ visual experience. To monitor the visual quality of PCs, point cloud quality assessment (PCQA) metrics are highly desired. Inspired by the video-based point cloud compression (V-PCC) standard to project a PC to planes and quantify the corresponding projection maps, a new blind PCQA metric based on V-PCC texture projection map and geometry projection map of PC is proposed for evaluating PC with compression distortion. Specifically, considering the combination of texture and geometry features of the projection maps to characterize distorted PC, a dual-stream convolutional network is designed from the perspective of global and local feature description to extract texture and geometry features of the distorted PC. The network mainly consists of two modules, that is, global texture and geometry fusion (GTGF) module and local geometry weighted local texture (LGWLT) module. The GTGF module is designed to realize the fusion of global texture and geometry features, which can describe PC's combination effect of texture and geometry distortions. Aiming at the relationship between texture and geometry distortions, the LGWLT module is designed to extract the features of local texture distortion caused by local geometry distortion. Furthermore, visual perception is combined to take distortions of the PC's salient regions into account in the process of PCQA. The experimental results on three PC databases show that the proposed PCQA metric is more consistent with visual perception, thus has greater potential than the representative PCQA metrics.

3D-Distortion Based Rate Distortion Optimization for Video-Based Point Cloud Compression

3D-Distortion Based Rate Distortion Optimization for Video-Based Point Cloud Compression

Efficient Geometry Surface Coding in V-PCC

In recent video-based point cloud compression (V-PCC), 3D point clouds are projected onto 2D images and compressed by High-Efficiency Video Coding (HEVC). However, HEVC was originally designed for natural visual signals, which is a suboptimal framework for point clouds. Therefore, there are still problems in geometry information compression in V-PCC: (1) The distortion based on the sum of squared error (SSE) in the existing rate-distortion optimization (RDO) is inconsistent with the geometric quality measurement; (2) The existing prediction cannot explore the fixed relationship between the corresponding far layer and near layer depth, which means that the far layer depth can be always not less than the corresponding near layer depth. In this paper, we present an efficient geometry surface coding (EGSC) method for V-PCC to address the problems. Firstly, an error projection (EP) model is designed to establish the relationship between the SSE-based distortion and the geometry quality metric. Secondly, an EP-based RDO is employed to improve the geometry information compression by estimating the point normals with gradients. Finally, an occupancy-map driven scheme is proposed to improve the prediction accuracy of merge modes. Experimental results show that the proposed method achieves an average of over 10% bit-rate saving compared with the V-PCC reference software.

Plenoptic Point Cloud Compression Using Multiview Extension of High Efficiency Video Coding

Plenoptic point clouds are more complete representations of three-dimensional (3-D) objects than single-color point clouds, as they can have multiple colors per spatial point, representing colors of each point as seen from different view angles. They are more realistic but also involve a larger volume of data in need of compression. Therefore, in this paper, a multiview-video-based framework is proposed to better exploit correlations in color across different viewpoints. To the best of the authors' knowledge, this is the first work to exploit correlations in color across different viewpoints using a multiview-video-based framework. In addition, it is observed that some unoccupied pixels, which do not have corresponding points in plenoptic point clouds and are of no use to the quality of the reconstructed plenoptic point cloud colors, may cost many bits. To address this problem, a block-based group smoothing and a combined occupancy-map-based rate distortion optimization and four-neighbor average residual padding are further proposed to reduce the bit cost of unoccupied color pixels. The proposed algorithms are implemented in the moving pictures experts group (MPEG) video-based point cloud compression (V-PCC) and multiview extension of High Efficiency Video Coding (MV-HEVC) reference software. The experimental results show that the proposed algorithms can lead to a Bjontegaard Delta bitrate (BD-rate) reduction of 40% compared with the RAHT-KLT. Compared with the V-PCC independently applied to each view direction, the proposed algorithms can provide a BD-rate reduction of over 70%.

No-Reference Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds

No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the information extracted from a bitstream for real-time and nonintrusive quality monitoring. We propose a no-reference bitstream-layer model for the perceptual quality assessment of video-based point cloud compression (V-PCC) encoded point clouds. First, we describe the fundamental relationship between perceptual coding distortion and the texture quantization parameter (TQP) when geometry encoding is lossless. Then, we incorporate the texture complexity (TC) into the proposed model while considering the fact that the perceptual coding distortion of a point cloud depends on the texture characteristics. TC is estimated using TQP and the texture bitrate per pixel (TBPP), both of which are extracted from the compressed bitstream without resorting to complete decoding. Then, we construct a texture distortion assessment model upon TQP and TBPP. By combining this texture distortion model with the geometry quantization parameter (GQP), we obtain an overall no-reference bitstream-layer PCQA model that we call bitstreamPCQ. Experimental results show that the proposed model markedly outperforms existing models in terms of widely used performance criteria, including the Pearson linear correlation coefficient (PLCC), the Spearman rank order correlation coefficient (SRCC) and the root mean square error (RMSE). The dataset developed in this study is publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-3.0.

Perceptually Weighted Rate Distortion Optimization for Video-Based Point Cloud Compression.

Dynamic point cloud is a volumetric visual data representing realistic 3D scenes for virtual reality and augmented reality applications. However, its large data volume has been the bottleneck of data processing, transmission, and storage, which requires effective compression. In this paper, we propose a Perceptually Weighted Rate-Distortion Optimization (PWRDO) scheme for Video-based Point Cloud Compression (V-PCC), which aims to minimize the perceptual distortion of reconstructed point cloud at the given bit rate. Firstly, we propose a general framework of perceptually optimized V-PCC to exploit visual redundancies in point clouds. Secondly, a multi-scale Projection based Point Cloud quality Metric (PPCM) is proposed to measure the perceptual quality of 3D point cloud. The PPCM model comprises 3D-to-2D patch projection, multi-scale structural distortion measurement, and fusion model. Approximations and simplifications of the proposed PPCM are also presented for both V-PCC integration and low complexity. Thirdly, based on the simplified PPCM model, we propose a PWRDO scheme with Lagrange multiplier adaptation, which is incorporated into the V-PCC to enhance the coding efficiency. Experimental results show that the proposed PPCM models can be used as standalone quality metrics, and they are able to achieve higher consistency with the human subjective scores than the state-of-the-art objective visual quality metrics. Also, compared with the latest V-PCC reference model, the proposed PWRDO-based V-PCC scheme achieves an average bit rate reduction of 13.52%, 8.16%, 10.56% and 9.54%, respectively, in terms of four objective visual quality metrics for point clouds. It is significantly superior to the state-of-the-art coding algorithms. The computational complexity of the proposed PWRDO increases by 1.71% and 0.05% on average to the V-PCC encoder and decoder, respectively, which is negligible. The source codes of the PPCM and PWRDO schemes are available at https://github.com/VVCodec/PPCM-PWRDO.

Rate-Distortion Optimal Transform Coefficient Selection for Unoccupied Regions in Video-Based Point Cloud Compression

This paper presents a novel method to determine rate-distortion optimized transform coefficients for efficient compression of videos generated from point clouds. The method exploits a generalized frequency selective extrapolation approach that iteratively determines rate-distortion-optimized coefficients for all basis functions of two-dimensional discrete cosine and sine transforms. The method is applied to blocks containing both occupied and unoccupied pixels in video based point cloud compression for HEVC encoding. In the proposed algorithm, only the values of the transform coefficients are changed such that resulting bit streams are compliant to the V-PCC standard. For all-intra coded point clouds, bitrate savings of more than 4% for geometry and more than 6% for texture error metrics with respect to standard encoding can be observed. These savings are more than twice as high as savings obtained using competing methods from literature. In the randomaccess case, our proposed method outperforms competing V-PCC methods by more than 0.5%.

Enhancement of 3D Point Cloud Contents Using 2D Image Super Resolution Network

Media technology has been developed to give users a sense of immersion. Recent media using 3D spatial data, such as augmented reality and virtual reality, has attracted attention. A point cloud is a data format that consists of a number of points, and thus can express 3D media using coordinates and color information for each point. Since a point cloud has a larger capacity than 2D images, a technology to compress the point cloud is required, i.e., standardized in the international standard organization MPEG as a video-based point cloud compression (V-PCC). V-PCC decomposes 3D point cloud data into 2D patches along orthogonal directions, and those patches are placed into a 2D image sequence, and then compressed using existing 2D video codecs. However, data loss may occur while converting a 3D point cloud into a 2D image sequence and encoding this sequence using a legacy video codec. This data loss can cause deterioration in the quality of a reconstructed point cloud. This paper proposed a method of enhancing a reconstructed point cloud by applying a super resolution network to the 2D patch image sequence of a 3D point cloud.

Video-Based Point Cloud Compression Artifact Removal

Photo-realistic point cloud capture and transmission are the fundamental enablers for immersive visual communication. The coding process of dynamic point clouds, especially video-based point cloud compression (V-PCC) developed by the MPEG standardization group, is now delivering state-of-the-art performance in compression efficiency. V-PCC is based on the projection of the point cloud patches to 2D planes and encoding the sequence as 2D texture and geometry patch sequences. However, the resulting quantization errors from coding can introduce compression artifacts, which can be very unpleasant for the quality of experience (QoE). In this work, we developed a novel out-of-the-loop point cloud geometry artifact removal solution that can significantly improve reconstruction quality without additional bandwidth cost. Our novel framework consists of a point cloud sampling scheme, an artifact removal network, and an aggregation scheme. The point cloud sampling scheme employs a cube-based neighborhood patch extraction to divide the point cloud into patches. The geometry artifact removal network then processes these patches to obtain artifact-removed patches. The artifact-removed patches are then merged together using an aggregation scheme to obtain the final artifact-removed point cloud. We employ 3D deep convolutional feature learning for geometry artifact removal that jointly recovers both the quantization direction and the quantization noise level by exploiting projection and quantization prior. The simulation results demonstrate that the proposed method is highly effective and can considerably improve the quality of the reconstructed point cloud.

Learning-Based Rate Control for Video-Based Point Cloud Compression.

Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches.

Differential Transform for Video-Based Plenoptic Point Cloud Coding.

Point cloud compression has been studied in standard bodies and we are here concerned with the Moving Picture Experts Group video-based point cloud compression (V-PCC) solution. Plenoptic point clouds (PPC) is a novel volumetric data representation wherein points are associated with colors in all viewing directions to improve realism. It is sampled as a number ( Nc ) of attribute colors per point. We propose a new method for the efficient video-based compression of PPC that is backwards compatible with the existing single-color V-PCC decoder. V-PCC generates three image atlases which are encoded using an image/video encoder. We assume there may be a reference color which is to be encoded as the main payload. We generate Nc+3 atlases and we produce Nc differential images against the reference color image. Those difference images are pixel-wise transformed using an Nc -point discrete cosine transform, generating Nc transformed atlases which are encoded, forming the secondary payload. Such secondary information is the plenoptic enhancement to the point cloud. If there is no reference attribute, we skip the differences and use the lowest frequency of the transformed atlases as the main payload. Results are presented that show an unrivaled performance of the proposed method.

Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure

The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency. In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance. We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud. The removed parts will be formed as new patches and added to the patch collection of the current point cloud. Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit. Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence. Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.

MPEG Immersive Video Coding Standard

This article introduces the ISO/IEC MPEG Immersive Video (MIV) standard, MPEG-I Part 12, which is undergoing standardization. The draft MIV standard provides support for viewing immersive volumetric content captured by multiple cameras with six degrees of freedom (6DoF) within a viewing space that is determined by the camera arrangement in the capture rig. The bitstream format and decoding processes of the draft specification along with aspects of the Test Model for Immersive Video (TMIV) reference software encoder, decoder, and renderer are described. The use cases, test conditions, quality assessment methods, and experimental results are provided. In the TMIV, multiple texture and geometry views are coded as atlases of patches using a legacy 2-D video codec, while optimizing for bitrate, pixel rate, and quality. The design of the bitstream format and decoder is based on the visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC) standard, MPEG-I Part 5.

Compression of Sparse and Dense Dynamic Point Clouds—Methods and Standards

In this article, a survey of the point cloud compression (PCC) methods by organizing them with respect to the data structure, coding representation space, and prediction strategies is presented. Two paramount families of approaches reported in the literature-the projection- and octree-based methods-are proven to be efficient for encoding dense and sparse point clouds, respectively. These approaches are the pillars on which the Moving Picture Experts Group Committee developed two PCC standards published as final international standards in 2020 and early 2021, respectively, under the names: video-based PCC and geometry-based PCC. After surveying the current approaches for PCC, the technologies underlying the two standards are described in detail from an encoder perspective, providing guidance for potential standard implementors. In addition, experiment evaluations in terms of compression performances for both solutions are provided.

Deep Learning Geometry Compression Artifacts Removal for Video-Based Point Cloud Compression

Deep Learning Geometry Compression Artifacts Removal for Video-Based Point Cloud Compression

Convolutional Neural Network-Based Occupancy Map Accuracy Improvement for Video-Based Point Cloud Compression

In video-based point cloud compression (V-PCC), a dynamic point cloud is projected onto geometry and attribute videos patch by patch for compression. In addition to the geometry and attribute videos, an occupancy map video is compressed into a V-PCC bitstream to indicate whether a two-dimensional (2D) point in the projected geometry video corresponds to any point in three-dimensional (3D) space. The occupancy map video is usually downsampled before compression to obtain a tradeoff between the bitrate and the reconstructed point cloud quality. Due to the accuracy loss in the downsampling process, some noisy points are generated, which leads to severe objective and subjective quality degradation of the reconstructed point cloud. To improve the quality of the reconstructed point cloud, we propose using a convolutional neural network (CNN) to improve the accuracy of the occupancy map video. We mainly make the following contributions. First, we improve the accuracy of the occupancy map video by formulating the problem as a binary segmentation problem since the pixel values of the occupancy map video are either 0 or 1. Second, in addition to the downsampled occupancy map video, we introduce a reconstructed geometry video as the other input of the CNN to provide more useful information in order to indicate the occupancy map video. To the best of our knowledge, this is the first learning-based work to improve the performance of V-PCC. Compared to state-of-the-art schemes, our proposed CNN-based approach achieves much more accurate occupancy map videos and significant bitrate savings.

V-PCC Performance Evaluation of the First MPEG Point Codec

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Representation of 3D scenes has become increasingly important in several industries using virtual and augmented reality technologies. The point cloud format is well suited for such representations. Indeed, point clouds can be created with a simple capture process and modest processing, enabling a realtime, end-to-end point cloud distribution chain. However, point cloud compression is required to obtain data rates and file sizes that could be economically viable for the industry. Standardization is required to ensure interoperability. In 2021, the International Organization for Standardization (ISO) Moving Picture Experts Group (MPEG) plans to publish a standard for its first point cloud codec, MPEG-I Part 5: Visual Volumetric Video-based Coding (V3C) and Video-based Point Cloud Compression (V-PCC). This standard enables a world of new services and applications, including cultural heritage, telepresence, and new forms of entertainment. In this paper, we review the principal use cases targeted by the V-PCC standard. We present the architecture of the V-PCC codec and describe its main tools, by giving insights into complexity at the encoder and decoder levels and explaining profiles and conformance points in V-PCC. We then present the methodology established, as a collaboration between industry and academics, for the evaluation of the V-PCC codec performance and the methodology’s origins. This methodology was applied to the MPEG point cloud compression test model software (named TMC2) to consistently evaluate technologies proposed during the standardization process. Finally, we compare the performance of the main V-PCC tools available for lossy and lossless compression. Finally, the conclusion provides elements in favor of a near-term deployment of V-PCC in the media industry</i> .

Performance Evaluation of the Codec Agnostic Approach in MPEG-I Video-Based Point Cloud Compression

In this study, we evaluated the codec agnostic approach of video-based point cloud compression (V-PCC) by applying several video codecs to V-PCC. The main concept of V-PCC is to use a video codec to compress the 2D patch images generated from the 3D point cloud. As a new immersive media standard of the Moving Picture Experts Group (MPEG), V-PCC is designed to support the codec agnostic approach, which can be employed to compress point cloud data by using any video codec. At present, the V-PCC reference software is designed by using MPEG High-Efficiency Video Coding. We extended the evaluation of the video codec applicability for PCC with well-known MPEG video coding standards, such as Advanced Video Coding, Essential Video Coding, and Versatile Video Coding. During the evaluation, we identified several key strategies for applying a video codec to V-PCC to maximize compression efficiency or computational complexity. Furthermore, the coding efficiency and time complexity of each codec were tested. The evaluation tests revealed that V-PCC supports the codec agnostic approach and that the performance of the video codec has a positive correlation with the V-PCC final coding efficiency. Reviewing these key strategies would help to develop V-PCC with different video codecs based on their profiles and levels.

Denoising and Inpainting for Point Clouds Compressed by V-PCC

With the development of immersive video, the quality of compressed 3D content has become an important issue. Video-based Point Cloud Compression (V-PCC) is a popular compression method for point cloud sequences; it achieves the highest quality among MPEG proposals. Compressed point clouds suffer from various artifacts when a high quantization parameter (QP) is used. Examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach provides significantly improved visual quality, and the improvement becomes more obvious with increasing QP values. Objective evaluation with point-to-point Mean Squared Error (p2p-MSE) shows our proposed method can improve point cloud quality and provides competitive results with lower complexity compared with other methods for point cloud outlier removal and inpainting.

Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression

The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-the-art international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively.