Image Compression With Encoder-Decoder Matched Semantic Segmentation

Trinh Man Hoang, Jinjia Zhou, Yibo Fan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 160-161

Abstract


In recent years, the layered image compression is demonstrated to be a promising direction, which encodes a compact representation of the input image and apply an up-sampling network to reconstruct the image. To further improve the quality of the reconstructed image, some works transmit the semantic segment together with the compressed image data. Consequently, the compression ratio is also decreased because extra bits are required for transmitting the semantic segment. To solve this problem, we propose a new layered image compression framework with encoder-decoder matched semantic segmentation (EDMS). And then, followed by the semantic segmentation, a special convolution neural network is used to enhance the inaccurate semantic segment. As a result, the accurate semantic segment can be obtained in the decoder without requiring extra bits. The experimental results show that the proposed EDMS framework can get up to 35.31% BD-rate reduction over the HEVC-based (BPG) codec, 5% bitrate and 24% encoding time saving compare to the state-of-the-art semantic-based image codec.

Related Material


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[bibtex]
@InProceedings{Hoang_2020_CVPR_Workshops,
author = {Hoang, Trinh Man and Zhou, Jinjia and Fan, Yibo},
title = {Image Compression With Encoder-Decoder Matched Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}