Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator
In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious ne...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11037781/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850078802633293824 |
|---|---|
| author | Daniele Mari Andre F. R. Guarda Nuno M. M. Rodrigues Simone Milani Fernando Pereira |
| author_facet | Daniele Mari Andre F. R. Guarda Nuno M. M. Rodrigues Simone Milani Fernando Pereira |
| author_sort | Daniele Mari |
| collection | DOAJ |
| description | In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration. |
| format | Article |
| id | doaj-art-4e868bb80b8e44c39b09d04c0ee797c3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e868bb80b8e44c39b09d04c0ee797c32025-08-20T02:45:28ZengIEEEIEEE Access2169-35362025-01-011310802510804210.1109/ACCESS.2025.358068011037781Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability EstimatorDaniele Mari0https://orcid.org/0000-0003-0727-3725Andre F. R. Guarda1https://orcid.org/0000-0001-5996-1074Nuno M. M. Rodrigues2https://orcid.org/0000-0001-9536-1017Simone Milani3https://orcid.org/0000-0001-8266-5839Fernando Pereira4https://orcid.org/0000-0001-6100-947XDepartment of Information Engineering, University of Padova, Padua, ItalyInstituto de Telecomunicações, Lisbon, PortugalInstituto de Telecomunicações, Lisbon, PortugalDepartment of Information Engineering, University of Padova, Padua, ItalyInstituto de Telecomunicações, Lisbon, PortugalIn the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration.https://ieeexplore.ieee.org/document/11037781/Point cloud geometry codingJPEG Pleno PCCdeep learning-based codecscalable coding |
| spellingShingle | Daniele Mari Andre F. R. Guarda Nuno M. M. Rodrigues Simone Milani Fernando Pereira Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator IEEE Access Point cloud geometry coding JPEG Pleno PCC deep learning-based codec scalable coding |
| title | Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator |
| title_full | Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator |
| title_fullStr | Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator |
| title_full_unstemmed | Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator |
| title_short | Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator |
| title_sort | point cloud geometry scalable coding using a resolution and quality conditioned latents probability estimator |
| topic | Point cloud geometry coding JPEG Pleno PCC deep learning-based codec scalable coding |
| url | https://ieeexplore.ieee.org/document/11037781/ |
| work_keys_str_mv | AT danielemari pointcloudgeometryscalablecodingusingaresolutionandqualityconditionedlatentsprobabilityestimator AT andrefrguarda pointcloudgeometryscalablecodingusingaresolutionandqualityconditionedlatentsprobabilityestimator AT nunommrodrigues pointcloudgeometryscalablecodingusingaresolutionandqualityconditionedlatentsprobabilityestimator AT simonemilani pointcloudgeometryscalablecodingusingaresolutionandqualityconditionedlatentsprobabilityestimator AT fernandopereira pointcloudgeometryscalablecodingusingaresolutionandqualityconditionedlatentsprobabilityestimator |