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Recently deep learning-based image compression has shown the potential tooutperform traditional codecs. However, most existing methods train multiplenetworks for multiple bit rates, which increase the implementation complexity.In this paper, we propose a new variable-rate image compression framework,which employs generalized octave convolutions (GoConv) and generalized octavetransposed-convolutions (GoTConv) with built-in generalized divisivenormalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- andGoTConv-based residual blocks are also developed in the encoder and decodernetworks. Our scheme also uses a stochastic rounding-based scalar quantization.To further improve the performance, we encode the residual between the inputand the reconstructed image from the decoder network as an enhancement layer.To enable a single model to operate with different bit rates and to learnmulti-rate image features, a new objective function is introduced. Experimentalresults show that the proposed framework trained with variable-rate objectivefunction outperforms the standard codecs such as H.265/HEVC-based BPG andstate-of-the-art learning-based variable-rate methods.

Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks