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BlockCNN: A Deep Network for Artifact Removal and Image Compression
AbstractWe present a general technique that performs both artifact removal and image compression. For artifact removal, we input a JPEG image and try to remove its compression artifacts. For compression, we input an image and process its 8 x 8 blocks in a sequence. For each block, we first try to predict its intensities based on previous blocks; then, we store a residual with respect to the input image. Our technique reuses JPEG's legacy compression and decompression routines. Both our artifact removal and our image compression techniques use the same deep network, but with different training weights. Our technique is simple and fast and it significantly improves the performance of artifact removal and image compression.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Maleki_2018_CVPR_Workshops,
author = {Maleki, Danial and Nadalian, Soheila and Mahdi Derakhshani, Mohammad and Amin Sadeghi, Mohammad},
title = {BlockCNN: A Deep Network for Artifact Removal and Image Compression},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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