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학술대회 Image Coding based on Selective Super-Resolution Network
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김연희, 조승현, 정세윤, 최진수
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1150-1154
20HH2300, [통합과제] 초실감 테라미디어를 위한 AV부호화 및 LF미디어 원천기술 개발, 최진수
This paper presents an image compression method using deep learning based super-resolution. Different from traditional image compression methods, we propose to select the super resolution network to enhance the perceptual quality. In the proposed method, the network selection map is generated by utilizing the semantic segmentation, image classification, and encoding distortion. Compared with traditional scalable image coding methods, more texture area are visually pleased even in the severe low bitrate encoding. For this reason, our method has the potential to result better compression ratio in terms of subjective image quality. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding (HEVC), our method achieve the average of 43.42% bitrate saving at the same perceptual image quality.
image compression, perceptual quality, scalable coding, super resolution
KSP 제안 키워드
Compression method, Image Compression, Image classification, Network selection, Perceptual Quality, Selection map, Semantic segmentation, Super resolution, compression ratio, deep learning(DL), high efficiency video coding