SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00569938" target="_blank" >RIV/67985556:_____/22:00569938 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CVPR52688.2022.00074" target="_blank" >http://dx.doi.org/10.1109/CVPR52688.2022.00074</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52688.2022.00074" target="_blank" >10.1109/CVPR52688.2022.00074</a>
Alternative languages
Result language
angličtina
Original language name
SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention
Original language description
We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
ISBN
978-1-6654-6946-3
ISSN
1063-6919
e-ISSN
—
Number of pages
10
Pages from-to
661-670
Publisher name
IEEE
Place of publication
Piscataway
Event location
New Orleans
Event date
Jun 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000867754200066