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Task-Aware Quantization Network for JPEG Image Compression
DC Field | Value | Language |
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dc.contributor.author | Choi, Jinyoung | - |
dc.contributor.author | Han, Bohyung | - |
dc.date.accessioned | 2023-12-11T01:14:15Z | - |
dc.date.available | 2023-12-11T01:14:15Z | - |
dc.date.created | 2021-09-17 | - |
dc.date.issued | 2020-01 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12365 LNCS, pp.309-324 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/10371/197945 | - |
dc.description.abstract | © 2020, Springer Nature Switzerland AG.We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks—two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses—two for semantic image understanding and another two for conventional image compression—and demonstrate the effectiveness of our approach to the individual tasks. | - |
dc.language | 영어 | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Task-Aware Quantization Network for JPEG Image Compression | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-030-58565-5_19 | - |
dc.citation.journaltitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.identifier.scopusid | 2-s2.0-85097374748 | - |
dc.citation.endpage | 324 | - |
dc.citation.startpage | 309 | - |
dc.citation.volume | 12365 LNCS | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Han, Bohyung | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Adaptive quantization | - |
dc.subject.keywordAuthor | Bitrate approximation | - |
dc.subject.keywordAuthor | JPEG image compression | - |
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