# Learned Block-based Hybrid Image Compression

arXiv: https://arxiv.org/abs/2012.09550 · HTML: https://mlsanity.com/paper/2012.09550/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 30 of 30)

- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [MLIC: Multi-Reference Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2211.07273/) (2022)
- [MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2307.15421/) (2023)
- [Image Coding for Machines with Omnipotent Feature Learning](https://mlsanity.com/paper/2207.01932/) (2022)
- [HST: Hierarchical Swin Transformer for Compressed Image Super-resolution](https://mlsanity.com/paper/2208.09885/) (2022)
- [PromptCIR: Blind Compressed Image Restoration with Prompt Learning](https://mlsanity.com/paper/2404.17433/) (2024)
- [SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment](https://mlsanity.com/paper/2205.04264/) (2022)
- [Content Adaptive Latents and Decoder for Neural Image Compression](https://mlsanity.com/paper/2212.10132/) (2022)
- [MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs](https://mlsanity.com/paper/2408.11758/) (2024)
- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt](https://mlsanity.com/paper/2407.13108/) (2024)
- [GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression](https://mlsanity.com/paper/2405.01170/) (2024)
- [Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning](https://mlsanity.com/paper/2504.01655/) (2025)
- [An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks](https://mlsanity.com/paper/2501.04329/) (2025)
- [STanH : Parametric Quantization for Variable Rate Learned Image Compression](https://mlsanity.com/paper/2410.00557/) (2024)
- [End-to-End Learned Block-Based Image Compression with Block-Level Masked Convolutions and Asymptotic Closed Loop Training](https://mlsanity.com/paper/2203.11686/) (2022)
- [Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression](https://mlsanity.com/paper/2305.07678/) (2023)
- [Approximately Invertible Neural Network for Learned Image Compression](https://mlsanity.com/paper/2408.17073/) (2024)
- [Continuous Patch Stitching for Block-wise Image Compression](https://mlsanity.com/paper/2502.16795/) (2025)
- [Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization](https://mlsanity.com/paper/2503.04832/) (2025)
- [Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields](https://mlsanity.com/paper/2504.21814/) (2025)
- [Streaming-capable High-performance Architecture of Learned Image Compression Codecs](https://mlsanity.com/paper/2208.01641/) (2022)
- [Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression](https://mlsanity.com/paper/2208.11673/) (2022)
- [Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression](https://mlsanity.com/paper/2307.02273/) (2023)
- [Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression](https://mlsanity.com/paper/2308.08723/) (2023)
- [Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent](https://mlsanity.com/paper/2508.15243/) (2025)
- [Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment](https://mlsanity.com/paper/2512.10450/) (2025)
- [ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation](https://mlsanity.com/paper/2603.10188/) (2026)
- [Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization](https://mlsanity.com/paper/2606.13366/) (2026)

## Abstract (arXiv)

Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. First, parallel acceleration of the autoregressive entropy model cannot be achieved due to serial decoding. Second, full-resolution inference often causes the out-of-memory(OOM) problem with limited GPU resources, especially for high-resolution images. Block partition is a good design choice to handle the above issues, but it brings about new challenges in reducing the redundancy between blocks and eliminating block effects. To tackle the above challenges, this paper provides a learned block-based hybrid image compression (LBHIC) framework. Specifically, we introduce explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks. Superior to context modeling by linear weighting of neighbor pixels in traditional codecs, we propose a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction. Moreover, to alleviate blocking artifacts, we further propose a boundary-aware postprocessing module (BPM) with the edge importance taken into account. Extensive experiments demonstrate that the proposed LBHIC codec outperforms the VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by approximately 86.7% compared with that of state-of-the-art learned image compression methods.