Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Ioan, Tabus"'
Autor:
Emre C. Kaya, Ioan Tabus
Publikováno v:
IEEE Access, Vol 10, Pp 83678-83691 (2022)
In this paper we propose a new paradigm for encoding the geometry of dense point cloud sequences, where a convolutional neural network (CNN), which estimates the encoding distributions, is optimized on several frames of the sequence to be compressed.
Externí odkaz:
https://doaj.org/article/43b1eb7c376c47549f036cc9e7501028
Autor:
Ioan Tabus, Emanuele Palma
Publikováno v:
IEEE Access, Vol 9, Pp 31092-31103 (2021)
We propose a codec for the lossless compression of plenoptic camera sensor images. The proposed encoder starts by splitting the input lenslet image into rectangular patches, with each patch corresponding to a microlens image. The encoder and decoder
Externí odkaz:
https://doaj.org/article/be4a32e5b4b64080b855420ca4e64cc3
Autor:
Pekka Astola, Ioan Tabus
Publikováno v:
IEEE Access, Vol 7, Pp 176820-176837 (2019)
In this paper we introduce a new light field codec, dubbed WaSPR (warping and sparse prediction on regions), which has additional features and improved performance when compared to our recently introduced WaSP (warping and sparse prediction) codec. W
Externí odkaz:
https://doaj.org/article/884c50a89390404d83ed936289b9c17c
Autor:
Ioan Tabus, Emre Can Kaya
We propose a new paradigm for encoding the geometry of point cloud sequences, where the convolutional neural network (CNN) which estimates the encoding distributions is optimized on several frames of the sequence to be compressed. We adopt lightweigh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19921ed605acabec4574f9ddb1ca9c1e
http://arxiv.org/abs/2206.01297
http://arxiv.org/abs/2206.01297
Autor:
Emre Can Kaya, Ioan Tabus
This paper describes a novel lossless point cloud compression algorithm that uses a neural network for estimating the coding probabilities for the occupancy status of voxels, depending on wide three dimensional contexts around the voxel to be encoded
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af85b6d426f43992e80a2cf2b8f72576
http://arxiv.org/abs/2106.06482
http://arxiv.org/abs/2106.06482
This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::315fd78dab2d702ca1af115902fdb3b1
http://arxiv.org/abs/2106.00828
http://arxiv.org/abs/2106.00828
Publikováno v:
EUSIPCO
2020 28th European Signal Processing Conference (EUSIPCO)
2020 28th European Signal Processing Conference (EUSIPCO)
This contribution presents the subjective evaluation of the compressed light field datasets obtained with four state-of-the-art codecs: two from the JPEG Pleno Light Field Verification Model and two recent methods for which codecs are publicly avai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93cd26fd18a5eafad4d6b5c3e413fb30
https://doi.org/10.23919/eusipco47968.2020.9287564
https://doi.org/10.23919/eusipco47968.2020.9287564
Autor:
Emanuele Palma, Ioan Tabus
Publikováno v:
2021 International Symposium on Signals, Circuits and Systems (ISSCS)
We propose two lossless archiving methods for the light field (LF) array of views created by plenoptic cameras, when the camera sensor images (also called lenslet images) are available. The two archiving methods are based on generative mechanisms, wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d940990b5566e685e1b25fad0086b43
Publikováno v:
MMSP
The paper proposes a new lossy way of encoding the geometry of point clouds. The proposed scheme reconstructs the geometry from only the two depth maps associated to a single projection direction and then proposes a progressive reconstruction process
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d82a8bdf67e8d4631bbb95a48e09e491
https://doi.org/10.1109/mmsp48831.2020.9287106
https://doi.org/10.1109/mmsp48831.2020.9287106
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 11:1146-1161
This paper proposes a complete lossless compression method for exploiting the redundancy of rectified light-field data. The light-field data consist of an array of rectified subaperture images, called for short views, which are segmented into regions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a662217870b37eaf9c95d8fffe568eb9
https://doi.org/10.1109/jstsp.2017.2737967
https://doi.org/10.1109/jstsp.2017.2737967