TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Adaptive Importance Channel Selection for Perceptual Image Compression |
| 영문제목(English Title) |
Adaptive Importance Channel Selection for Perceptual Image Compression |
| 저자(Author) |
Yifan He
Feng Li
Huihui Bai
Yao Zhao
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| 원문수록처(Citation) |
VOL 14 NO. 09 PP. 3823 ~ 3840 (2020. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.
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| 키워드(Keyword) |
Image compression
Auto-encoder
Perceptual loss
Bit-allocate strategy
Importance map
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| 파일첨부 |
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