Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest codebook items and train index predictor with code-level supervision. Due to the richness of visual signal, VQ encoding often leads to large quantization error. Furthermore, training predictor with code-level supervision can not take the final reconstruction errors into consideration, result in sub-optimal prior modeling accuracy. In this paper we address the above two issues and propose a Texture Vector-Quantization and a Reconstruction Aware Prediction strategy. The texture vector-quantization strategy leverages the task character of super-resolution and only introduce codebook to model the prior of missing textures. While the reconstruction aware prediction strategy makes use of the straight-through estimator to directly train index predictor with image-level supervision. Our proposed generative SR model (TVQ&RAP) is able to deliver photo-realistic SR results with small computational cost.
@article{arxiv.2509.23774,
title = {Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution},
author = {Qifan Li and Jiale Zou and Jinhua Zhang and Wei Long and Xingyu Zhou and Shuhang Gu},
journal= {arXiv preprint arXiv:2509.23774},
year = {2026}
}