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

arXiv: https://arxiv.org/abs/2012.15463 · HTML: https://mlsanity.com/paper/2012.15463/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Learned Video Compression via Heterogeneous Deformable Compensation Network](https://mlsanity.com/paper/2207.04589/) (2022)
- [LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression](https://mlsanity.com/paper/2304.09571/) (2023)
- [Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression](https://mlsanity.com/paper/2503.21284/) (2025)
- [Frequency Disentangled Residual Network](https://mlsanity.com/paper/2109.12556/) (2021)
- [Online Meta Adaptation for Variable-Rate Learned Image Compression](https://mlsanity.com/paper/2111.08256/) (2021)
- [An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding](https://mlsanity.com/paper/2401.12587/) (2024)
- [3DM-WeConvene: Learned Image Compression with 3D Multi-Level Wavelet-Domain Convolution and Entropy Model](https://mlsanity.com/paper/2504.04658/) (2025)
- [FEDS: Feature and Entropy-Based Distillation Strategy for Efficient Learned Image Compression](https://mlsanity.com/paper/2503.06399/) (2025)
- [Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework](https://mlsanity.com/paper/2511.08915/) (2025)
- [Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression](https://mlsanity.com/paper/2605.14391/) (2026)
- [Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation](https://mlsanity.com/paper/2502.15188/) (2025)
- [ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2606.00111/) (2026)

## Abstract (arXiv)

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks 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 octave transposed-convolutions (GoTConv) with built-in generalized divisive normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and GoTConv-based residual blocks are also developed in the encoder and decoder networks. Our scheme also uses a stochastic rounding-based scalar quantization. To further improve the performance, we encode the residual between the input and the reconstructed image from the decoder network as an enhancement layer. To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.