Visual Quality Optimization for View-Dependent Point Cloud Compression

Wang, Danying; Zhu, Wen-Hong; Xu, Yingzhan; Xu, Yiling; LeYang,

doi:10.1109/iscas51556.2021.9401619

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…However, sacrificing other views can only improve the main view's quality. A similar attempt has also been made in 47 to improve the main view for the user by saving more points for the main view patches. They could keep points from being seen from other views, improving the main view while boosting compression bitrate.…”

Section: Enhancing Main View Quality In V-pccmentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

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Point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual realityand augmented reality. However, the point cloud, especially the dynamic one, must be compressed efficiently due to its hugedata volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D pointcloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches areprojected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity informationand some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps tomaintain the proximity of the points and retain more original points by exploiting density and occlusion of the points . The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods in terms of rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

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Section: Enhancing Main View Quality In V-pccmentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

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show abstract

“…However, it does not work on additional attributes such as reflection and material. There are also some other similar methods [31,32]. The idea of using patch packaging methods to improve occupancy maps is also worth studying.…”

Section: Estimate Normalsmentioning

confidence: 99%

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

Luo,

Chi,

et al. 2024

Applied Sciences

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High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%.

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“…Point cloud is a set of 3D points which contain both geometry content and attribute content. Most point cloud used in applications is continuously moving, which is called dynamic point cloud(DPC) [5], [6]. Several DPC models are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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

2023

KSII TIIS

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The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images. These images are then padded and compressed by High-Efficiency Video Coding(HEVC). Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels. Origin pixels are generated from projection of 3D point cloud. Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding. For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels. For unoccupied pixels, they are not reconstructed. The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates. However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction. In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels. Firstly, these pixels are classified based on the occupancy map. Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions. Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction. The proposed method is applied to geometry frames. Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.

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Visual Quality Optimization for View-Dependent Point Cloud Compression

Cited by 8 publications

References 14 publications

Improved video-based point cloud compression via segmentation

Improved video-based point cloud compression via segmentation

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

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

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