# PO-ELIC: Perception-Oriented Efficient Learned Image Coding

arXiv: https://arxiv.org/abs/2205.14501 · HTML: https://mlsanity.com/paper/2205.14501/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2022.

## Built on by (most cited first, 30 of 36)

- [Lossy Image Compression with Conditional Diffusion Models](https://mlsanity.com/paper/2209.06950/) (2022)
- [Towards image compression with perfect realism at ultra-low bitrates](https://mlsanity.com/paper/2310.10325/) (2023)
- [GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting](https://mlsanity.com/paper/2403.08551/) (2024)
- [Multi-Realism Image Compression with a Conditional Generator](https://mlsanity.com/paper/2212.13824/) (2022)
- [High-Fidelity Image Compression with Score-based Generative Models](https://mlsanity.com/paper/2305.18231/) (2023)
- [StableCodec: Taming One-Step Diffusion for Extreme Image Compression](https://mlsanity.com/paper/2506.21977/) (2025)
- [Idempotence and Perceptual Image Compression](https://mlsanity.com/paper/2401.08920/) (2024)
- [Generative Latent Coding for Ultra-Low Bitrate Image and Video Compression](https://mlsanity.com/paper/2505.16177/) (2025)
- [Generative Latent Coding for Ultra-Low Bitrate Image Compression](https://mlsanity.com/paper/2512.20194/) (2025)
- [EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation](https://mlsanity.com/paper/2309.03244/) (2023)
- [Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder](https://mlsanity.com/paper/2404.04916/) (2024)
- [CoD: A Diffusion Foundation Model for Image Compression](https://mlsanity.com/paper/2511.18706/) (2025)
- [ECSIC: Epipolar Cross Attention for Stereo Image Compression](https://mlsanity.com/paper/2307.10284/) (2023)
- [Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder](https://mlsanity.com/paper/2512.12229/) (2025)
- [LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression](https://mlsanity.com/paper/2411.13033/) (2024)
- [A Lightweight Model for Perceptual Image Compression via Implicit Priors](https://mlsanity.com/paper/2502.13988/) (2025)
- [Synonymous Variational Inference for Perceptual Image Compression](https://mlsanity.com/paper/2505.22438/) (2025)
- [Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression](https://mlsanity.com/paper/2508.04979/) (2025)
- [Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis](https://mlsanity.com/paper/2403.16258/) (2024)
- [ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization](https://mlsanity.com/paper/2603.02897/) (2026)
- [What Matters in Practical Learned Image Compression](https://mlsanity.com/paper/2605.05148/) (2026)
- [Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models](https://mlsanity.com/paper/2401.07957/) (2024)
- [Conditional Hallucinations for Image Compression](https://mlsanity.com/paper/2410.19493/) (2024)
- [Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/2503.17558/) (2025)
- [Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression](https://mlsanity.com/paper/2401.14007/) (2024)
- [Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression](https://mlsanity.com/paper/2404.10234/) (2024)
- [Rethinking Learned Image Compression: Context is All You Need](https://mlsanity.com/paper/2407.11590/) (2024)
- [Hierarchical Semantic Compression for Consistent Image Semantic Restoration](https://mlsanity.com/paper/2502.16799/) (2025)
- [Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model](https://mlsanity.com/paper/2505.13152/) (2025)
- [VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression](https://mlsanity.com/paper/2512.15701/) (2025)

## Abstract (arXiv)

In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate reconstructions of LIC suffer from artifacts such as blurring, color drifting and texture missing. Moreover, those varied artifacts make image quality metrics correlate badly with human perceptual quality. In this paper, we propose PO-ELIC, i.e., Perception-Oriented Efficient Learned Image Coding. To be specific, we adapt ELIC, one of the state-of-the-art LIC models, with adversarial training techniques. We apply a mixture of losses including hinge-form adversarial loss, Charbonnier loss, and style loss, to finetune the model towards better perceptual quality. Experimental results demonstrate that our method achieves comparable perceptual quality with HiFiC with much lower bitrate.