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Channel-wise Vector Quantization
Wei Song, Ti · 2026-05-26 · via cs.AI updates on arXiv.org

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Abstract:We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch feature vector, CVQ quantizes each channel of the feature map. This formulation represents an image as discrete levels of visual details, rather than as a grid of spatial patches. Based on CVQ, we introduce a new visual autoregressive framework with "next-channel prediction". Instead of rendering images patch by patch in raster order, our Channel-wise Autoregressive (CAR) model predicts image channels sequentially, producing progressively enriched visual details. Specifically, it first sketches global structure and then refines fine-grained attributes, akin to a human artist's workflow. Empirically, we show that: (1) CVQ achieves 100% codebook utilization with a 16K+ codebook size without any bells and whistles, and substantially improves reconstruction quality over conventional VQ; and (2) CAR attains a DPG score of 86.7 and a GenEval score of 0.79, demonstrating strong effectiveness for text-to-image generation.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.26089 [cs.CV]
  (or arXiv:2605.26089v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2605.26089

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Wei Song [view email]
[v1] Mon, 25 May 2026 17:52:08 UTC (10,927 KB)