Performance assessment of point cloud compression

Mekuria, Rufael; Laserre, S.; Tulvan, Christian Liviu

doi:10.1109/vcip.2017.8305132

Cited by 50 publications

(30 citation statements)

References 3 publications

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“…To have a baseline for determining target bitrates and distortions, a recent hybrid octree-image point cloud codec for tele-immersive video [6] was chosen as anchor. Quality metrics described in [39], [40] were selected for the objective quality assessment. These metrics are referred as point-to-point (D1) and point to plane (D2) geometry distortion metrics.…”

Section: B Evaluation Methodologymentioning

confidence: 99%

Emerging MPEG Standards for Point Cloud Compression

Schwarz

Preda

Baroncini³

et al. 2019

IEEE J. Emerg. Sel. Topics Circuits Syst.

Self Cite

546

341

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Due to the increased popularity of augmented and virtual reality experiences, the interest in capturing the real world in multiple dimensions and in presenting it to users in an immersible fashion has never been higher. Distributing such representations enables users to freely navigate in multi-sensory 3D media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today's networks. Efficient compression technologies well adopted in the content chain are in high demand and are key components to democratize augmented and virtual reality applications. The Moving Picture Experts Group, MPEG, as one of the main standardization groups dealing with multimedia, identified the trend and started recently the process of building an open standard for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels. This paper introduces the main developments and technical aspects of this ongoing standardization effort.

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Section: B Evaluation Methodologymentioning

confidence: 99%

Emerging MPEG Standards for Point Cloud Compression

Schwarz

Preda

Baroncini³

et al. 2019

IEEE J. Emerg. Sel. Topics Circuits Syst.

Self Cite

546

341

View full text Add to dashboard Cite

show abstract

“…Table II and Fig. 8 show Bjontegaard Functions (BD-Rate) [29] of the Queen sequence with different CHD threshold values, where the BD-Rates in D1 and D2 show the geometry position error, and the Luma, Cb and Cr show the color error as described in [30] [31]. As the CHD threshold value increases, the D1 and Y PSNR values increase as well.…”

Section: Contextual Homogeneity-based Patch Decomposition (Chd)mentioning

confidence: 99%

Contextual Homogeneity-Based Patch Decomposition Method for Higher Point Cloud Compression

Rhyu

Kim

et al. 2020

IEEE Access

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“…Different aspects of the quality were tested, including overall quality of the 3D human rendition, the quality of the colors, the perceived realism of the point cloud, the motion quality, the near/far quality, and the level of immersiveness with the observer comparing the avatar and the point cloud representations. Mekuria et al 8 provides bitrate results and objective scores for their proposed point cloud codec, 7 which was assessed in the framework of the recent activities of the MPEG standardization committee. The selected data set consists of colored objects and different encoding strategies were considered, providing bitrate results and objective scores.…”

Section: Related Workmentioning

confidence: 99%

Point cloud subjective evaluation methodology based on reconstructed surfaces

Alexiou

Pinheiro

Duarte

et al. 2018

Applications of Digital Image Processing XLI

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Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. Likewise, the correlation with a subjective test using a different representation of the point cloud data also showed poor correlation. These results suggest the need for more reliable objective quality metrics and further studies on adequate point cloud data representations.

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Performance assessment of point cloud compression

Cited by 50 publications

References 3 publications

Emerging MPEG Standards for Point Cloud Compression

Emerging MPEG Standards for Point Cloud Compression

Contextual Homogeneity-Based Patch Decomposition Method for Higher Point Cloud Compression

Point cloud subjective evaluation methodology based on reconstructed surfaces

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