In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible. At encoder side, we fine-tune a pre-trained artifact removal network on target data by using a compression objective applied on the weight-update. In particular, the compression objective encourages weight-updates which are sparse and closer to quantized values. This way, the final weight-update can be compressed more efficiently by pruning and quantization, and can be included into the encoded bitstream together with the image bitstream of a traditional codec. We show that this approach achieves reconstruction quality which is on-par or slightly superior to a traditional codec, at comparable bitrates. To our knowledge, this is the first attempt to combine image compression and neural network's weight update compression.
- Pub Date:
- May 2019
- Computer Science - Machine Learning;
- Computer Science - Multimedia;
- Statistics - Machine Learning
- Submission for CHALLENGE ON LEARNED IMAGE COMPRESSION (CLIC) 2019 (updated on 14 June 2019)