  We 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 by
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.
