# Content-oriented learned image compression

arXiv: https://arxiv.org/abs/2207.14168 · HTML: https://mlsanity.com/paper/2207.14168/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 22 of 22)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Residual Non-local Attention Networks for Image Restoration](https://mlsanity.com/paper/1903.10082/) (2019)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation](https://mlsanity.com/paper/2003.02012/) (2020)
- [Learning Convolutional Networks for Content-weighted Image Compression](https://mlsanity.com/paper/1703.10553/) (2017)
- [Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton](https://mlsanity.com/paper/2103.15368/) (2021)
- [CompressNet: Generative Compression at Extremely Low Bitrates](https://mlsanity.com/paper/2006.08003/) (2020)
- [Pixel Deconvolutional Networks](https://mlsanity.com/paper/1705.06820/) (2017)

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

- [Multi-Realism Image Compression with a Conditional Generator](https://mlsanity.com/paper/2212.13824/) (2022)
- [LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression](https://mlsanity.com/paper/2304.12319/) (2023)
- [EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation](https://mlsanity.com/paper/2309.03244/) (2023)
- [High Visual-Fidelity Learned Video Compression](https://mlsanity.com/paper/2310.04679/) (2023)
- [Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression](https://mlsanity.com/paper/2411.16119/) (2024)
- [COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation](https://mlsanity.com/paper/2410.01698/) (2024)
- [CADC: Content Adaptive Diffusion-Based Generative Image Compression](https://mlsanity.com/paper/2602.21591/) (2026)
- [MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field](https://mlsanity.com/paper/2309.13607/) (2023)
- [HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform](https://mlsanity.com/paper/2305.07519/) (2023)
- [FLLIC: Functionally Lossless Image Compression](https://mlsanity.com/paper/2401.13616/) (2024)
- [HyperVQ: Enabling Hyperprior Entropy Modeling for VQ-Based Generative Image Compression](https://mlsanity.com/paper/2512.07192/) (2025)
- [Clear-Weighted Bit Allocation for Satellite Downlinks](https://mlsanity.com/paper/2608.01457/) (2026)

## Abstract (arXiv)

In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are unlabeled and do not consider image semantics or content when optimizing the model. In fact, human eyes have different sensitivities to different content, so the image content also needs to be considered. In this paper, we propose a content-oriented image compression method, which handles different kinds of image contents with different strategies. Extensive experiments show that the proposed method achieves competitive subjective results compared with state-of-the-art end-to-end learned image compression methods or classic methods.