Image Compression-Aware Deep Camera ISP Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Uhm, Kwang-Hyun | - |
dc.contributor.author | Choi, Kyuyeon | - |
dc.contributor.author | Jung, Seung-Won | - |
dc.contributor.author | Ko, Sung-Jea | - |
dc.date.accessioned | 2022-03-11T21:40:32Z | - |
dc.date.available | 2022-03-11T21:40:32Z | - |
dc.date.created | 2022-01-20 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/138641 | - |
dc.description.abstract | Several recent studies have attempted to fully replace the conventional camera image signal processing (ISP) pipeline with convolutional neural networks (CNNs). However, the previous CNN-based ISPs, simply referred to as ISP-Nets, have not explicitly considered that images have to be lossy-compressed in most cases, especially by the off-the-shelf JPEG. To address this issue, in this paper, we propose a novel compression-aware deep camera ISP learning framework. At first, we introduce a new use case of compression artifacts simulation network (CAS-Net), which operates in the opposite way of commonly used compression artifacts reduction networks. Then, the CAS-Net is connected with an ISP-Net such that the ISP network can be trained with consideration of image compression. Throughout experimental studies, we show that our compression-aware camera ISP network can produce images with a better tradeoff between bit-rate and image quality compared to its compression-agnostic version when the performance is evaluated after JPEG compression. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | QUALITY ASSESSMENT | - |
dc.title | Image Compression-Aware Deep Camera ISP Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Seung-Won | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3116702 | - |
dc.identifier.scopusid | 2-s2.0-85116855907 | - |
dc.identifier.wosid | 000706821200001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.137824 - 137832 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 137824 | - |
dc.citation.endPage | 137832 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | QUALITY ASSESSMENT | - |
dc.subject.keywordAuthor | Image coding | - |
dc.subject.keywordAuthor | Transform coding | - |
dc.subject.keywordAuthor | Cameras | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Pipelines | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Noise reduction | - |
dc.subject.keywordAuthor | Camera ISP | - |
dc.subject.keywordAuthor | compression artifacts | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | image compression | - |
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