Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Dumic, Emil; Bjelopera, Anamaria; Nüchter, Andreas

doi:10.3390/s22010197

Cited by 8 publications

(5 citation statements)

References 37 publications

(49 reference statements)

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“…In contrast, geometry-based compression, G-PCC, encodes the content directly in the 3D space. While V-PCC applies the existing 2D video compression approach to a collection of various 2D pictures transformed from the 3D point data, GPCC applies Octree and K-D tree data structures to describe the placement of the points and their vicinities in the 3D space [28][29][30][31] . As V-PCC stands as the current state-of-the-art in dynamic point cloud compression, this section introduces recent literature focused on enhancing the efficiency of V-PCC:…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

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“…Different methods exist to compress point cloud data [ 33 , 34 , 35 ]; most of them perform compression of point cloud geometry using octree coding [ 36 ], where data are transformed into voxel representation, in order to appropriately exploit volumetric redundancy, and are then partitioned until sub-cubes of dimension one are reached; local approximations called “triangle soups” (trisoup) can be adopted, where the geometry can be represented by a pruned octree plus a surface model [ 36 , 37 ].…”

Section: Related Workmentioning

confidence: 99%

Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression

Adhuran,

Khan,

Martini

2024

Sensors

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Neuromorphic Vision Sensors (NVSs) are emerging sensors that acquire visual information asynchronously when changes occur in the scene. Their advantages versus synchronous capturing (frame-based video) include a low power consumption, a high dynamic range, an extremely high temporal resolution, and lower data rates. Although the acquisition strategy already results in much lower data rates than conventional video, NVS data can be further compressed. For this purpose, we recently proposed Time Aggregation-based Lossless Video Encoding for Neuromorphic Vision Sensor Data (TALVEN), consisting in the time aggregation of NVS events in the form of pixel-based event histograms, arrangement of the data in a specific format, and lossless compression inspired by video encoding. In this paper, we still leverage time aggregation but, rather than performing encoding inspired by frame-based video coding, we encode an appropriate representation of the time-aggregated data via point-cloud compression (similar to another one of our previous works, where time aggregation was not used). The proposed strategy, Time-Aggregated Lossless Encoding of Events based on Point-Cloud Compression (TALEN-PCC), outperforms the originally proposed TALVEN encoding strategy for the content in the considered dataset. The gain in terms of the compression ratio is the highest for low-event rate and low-complexity scenes, whereas the improvement is minimal for high-complexity and high-event rate scenes. According to experiments on outdoor and indoor spike event data, TALEN-PCC achieves higher compression gains for time aggregation intervals of more than 5 ms. However, the compression gains are lower when compared to state-of-the-art approaches for time aggregation intervals of less than 5 ms.

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“…To assess the visual quality of the compressed images generated, both objective and subjective methods of image quality evaluation are used [ 8 ]. These two types of methods are mentioned in many studies, and are used both in traditional and learning-based image codecs performance evaluation [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

Afnan

Ullah

Yaseen

et al. 2023

Sensors

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The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

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Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Cited by 8 publications

References 37 publications

Improved video-based point cloud compression via segmentation

Improved video-based point cloud compression via segmentation

Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

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