지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.11
- 수록면
- 352 - 355 (4page)
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초록· 키워드
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We propose an approach to enhance the local implicit neural network representation for decoding high-quality images. The JPEG algorithm quantizes coefficients in the discrete cosine frequency domain into a small set to achieve high compression ratios. Therefore, quality degradation is inevitable in traditional JPEG decoding methods. To improve the quality of decoded JPEG images, we introduce a continuous cosine coefficient extractor into the network. Through learning as a function of interval coordinates in JPEG, the proposed network can restore overall quality coefficients. This approach takes distorted cosine coefficients as input, restores the quantized coefficients, and applies them to an implicit neural network to decode high-quality images. As a result, the proposed method achieves state-of-the-art performance in terms of compressed image restoration for various quality coefficients with a single model.
목차
Abstract
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 실험
Ⅳ. 결론
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