http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
향상된 연속 코사인 계수 추출을 통한 국소적 함축 신경망 표현 방식의 앤드-투-앤드 JPEG 복호기
한우경(Woo Kyoung Han),임성훈(Sunghoon Im),김재덕(Jaedeok Kim),진경환 (Kyong Hwan Jin) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.11
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.