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- Xu ZouSuggested from co-authorship
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Works9 from public data
- High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation21
This work tries to tackle the issue of high-fidelity fine variable-rate image compression and proposes the Invertible Activation Transformation (IAT) module, which outperforms the state-of-the-art variable- rate image compression method by a large margin, especially after multiple re-encodings.
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- Make Lossy Compression Meaningful for Low-Light Images9
Experimental results show that the proposed joint solution achieves a significant improvement over different combinations of existing state-of-the-art sequential ``Compress before Enhance'' or ``Enhance before Compress'' solutions for low-light images, which would make lossy low-light image compression more meaningful.
- Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem8
A novel approach to single-image super-resolution (SISR) that balances perceptual quality and distortion through multi-objective optimization (MOO) and dynamically adjusts loss weights during training, which reduces the need for manual hyperparameter tuning and lessens computational demands compared to AutoML.
- Powerful Lossy Compression for Noisy Images4
This paper designs an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch, and demonstrates that the joint solution outperforms existing state-of-the-art methods.
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- R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment2
It is found that current VLMs are not sufficiently accurate in judging fine-grained CG quality, but that descriptions of visually similar images can significantly improve a VLM's understanding of a given CG image.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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