1 research outputs found
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks
In this paper, we propose an end-to-end mixed-resolution image compression
framework with convolutional neural networks. Firstly, given one input image,
feature description neural network (FDNN) is used to generate a new
representation of this image, so that this image representation can be more
efficiently compressed by standard codec, as compared to the input image.
Furthermore, we use post-processing neural network (PPNN) to remove the coding
artifacts caused by quantization of codec. Secondly, low-resolution image
representation is adopted for high efficiency compression in terms of most of
bit spent by image's structures under low bit-rate. However, more bits should
be assigned to image details in the high-resolution, when most of structures
have been kept after compression at the high bit-rate. This comes from a fact
that the low-resolution image representation can't burden more information than
high-resolution representation beyond a certain bit-rate. Finally, to resolve
the problem of error back-propagation from the PPNN network to the FDNN
network, we introduce to learn a virtual codec neural network to imitate two
continuous procedures of standard compression and post-processing. The
objective experimental results have demonstrated the proposed method has a
large margin improvement, when comparing with several state-of-the-art
approaches.Comment: 5 pages, and 2 figures. arXiv admin note: substantial text overlap
with arXiv:1712.0596