Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates...
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| Published in | IEEE transactions on image processing Vol. 30; pp. 6623 - 6636 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1057-7149 1941-0042 1941-0042 |
| DOI | 10.1109/TIP.2021.3096060 |
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| Abstract | In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0 . |
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| AbstractList | In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0 . In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0.In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0. |
| Author | Su, Honglei Yang, Huan Hou, Junhui Yuan, Hui Hamzaoui, Raouf Liu, Qi |
| Author_xml | – sequence: 1 givenname: Qi orcidid: 0000-0002-3958-9962 surname: Liu fullname: Liu, Qi email: sdqi.liu@gmail.com organization: School of Information Science and Engineering, Shandong University, Qingdao, China – sequence: 2 givenname: Hui orcidid: 0000-0001-5212-3393 surname: Yuan fullname: Yuan, Hui email: huiyuan@sdu.edu.cn organization: School of Control Science and Engineering, Shandong University, Jinan, China – sequence: 3 givenname: Raouf orcidid: 0000-0001-6699-7331 surname: Hamzaoui fullname: Hamzaoui, Raouf email: rhamzaoui@dmu.ac.uk organization: School of Engineering and Sustainable Development, De Montfort University, Leicester, U.K – sequence: 4 givenname: Honglei orcidid: 0000-0001-6144-4930 surname: Su fullname: Su, Honglei email: suhonglei@qdu.edu.cn organization: School of Electronic Information, Qingdao University, Qingdao, China – sequence: 5 givenname: Junhui orcidid: 0000-0003-3431-2021 surname: Hou fullname: Hou, Junhui email: jh.hou@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 6 givenname: Huan orcidid: 0000-0001-5810-0248 surname: Yang fullname: Yang, Huan email: cathy_huanyang@hotmail.com organization: College of Computer Science and Technology, Qingdao University, Qingdao, China |
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| Snippet | In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One... |
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| SubjectTerms | Bit rate Coders Computation Correlation coefficients Distortion Feature extraction Geometry Image color analysis Measurement Optimization perceptual quality metric Point cloud compression rate-distortion optimization subjective test Three-dimensional displays Video compression |
| Title | Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression |
| URI | https://ieeexplore.ieee.org/document/9490512 https://www.proquest.com/docview/2555726612 https://www.proquest.com/docview/2553522721 |
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