{"ID":2864256,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23774","arxiv_id":"2509.23774","title":"Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution","abstract":"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\u0026RAP) is able to deliver photo-realistic SR results with small computational cost.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.23774","url_pdf":"https://arxiv.org/pdf/2509.23774v3","authors":"[\"Qifan Li\",\"Jiale Zou\",\"Jinhua Zhang\",\"Wei Long\",\"Xingyu Zhou\",\"Shuhang Gu\"]","published":"2025-09-28T09:40:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}