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Task-Aware Quantization Network for JPEG Image Compression
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Cited 26 time in Scopus
- Authors
- Issue Date
- 2020-01
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12365 LNCS, pp.309-324
- Abstract
- © 2020, Springer Nature Switzerland AG.We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks—two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses—two for semantic image understanding and another two for conventional image compression—and demonstrate the effectiveness of our approach to the individual tasks.
- ISSN
- 0302-9743
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