TextBoost: Boosting Scene Text Fidelity in Ultra-low Bitrate Image Compression
Bingxin Wang, Yuan Lan, Zhaoyi Sun, Yang Xiang, Jie Sun

TL;DR
TextBoost introduces a semantic-guided image compression method that significantly improves small-font scene text fidelity at ultra-low bitrates without sacrificing overall image quality, by transmitting auxiliary OCR-based guidance.
Contribution
The paper proposes a novel approach that incorporates OCR-extracted textual guidance into image compression, enabling better preservation of scene text without relying on ROI coding.
Findings
Achieves up to 60.6% higher text-recognition F1 score at similar PSNR and bits per pixel.
Effectively enhances small-font text clarity while maintaining global image quality.
Decouples text fidelity from overall rate-distortion optimization.
Abstract
Ultra-low bitrate image compression faces a critical challenge: preserving small-font scene text while maintaining overall visual quality. Region-of-interest (ROI) bit allocation can prioritize text but often degrades global fidelity, leading to a trade-off between local accuracy and overall image quality. Instead of relying on ROI coding, we incorporate auxiliary textual information extracted by OCR and transmitted with negligible overhead, enabling the decoder to leverage this semantic guidance. Our method, TextBoost, operationalizes this idea through three strategic designs: (i) adaptively filtering OCR outputs and rendering them into a guidance map; (ii) integrating this guidance with decoder features in a calibrated manner via an attention-guided fusion block; and (iii) enforcing guidance-consistent reconstruction in text regions with a regularizing loss that promotes natural…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Advanced Image Processing Techniques · Image and Video Quality Assessment
