TLIC: Learned Image Compression with ROI-Weighted Distortion and Bit Allocation
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Abstract (arXiv)
This short paper describes our method for the track of image compression. To achieve better perceptual quality, we use the adversarial loss to generate realistic textures, use region of interest (ROI) mask to guide the bit allocation for different regions. Our Team name is TLIC.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 16 Jan 2024.
Builds on
15 of 19 references.
References · 15
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2018Variational image compression with a scale hyperpriorcited by 910
- 2020Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modulescited by 570
- 2020High-Fidelity Generative Image Compressioncited by 331
- 2022ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Codingcited by 296
- 2020Channel-wise Autoregressive Entropy Models for Learned Image Compressioncited by 255
- 2018Generative Adversarial Networks for Extreme Learned Image Compressioncited by 213
- 2018Context-aware Synthesis for Video Frame Interpolationcited by 153
- 2020Asymmetric Gained Deep Image Compression With Continuous Rate Adaptationcited by 83
- 2022MLIC: Multi-Reference Entropy Model for Learned Image Compressioncited by 80
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Citing papers per year: 2026: 2.
2 papers.