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 blending with the scene. Extensive experiments on TextOCR and ICDAR 2015 demonstrate that TextBoost yields up to 60.6% higher text-recognition F1 at comparable Peak Signal-to-Noise Ratio (PSNR) and bits per pixel (bpp), producing sharper small-font text while preserving global image quality and effectively decoupling text enhancement from global rate-distortion optimization.
Abstract (translated)
超低比特率图像压缩面临的一个关键挑战是，在保持整体视觉质量的同时，保留小字体场景文本。区域感兴趣（ROI）的比特分配可以优先处理文本，但这通常会降低全局保真度，导致局部准确性和整体图像质量之间的权衡。我们不依赖于ROI编码，而是结合由OCR提取并以几乎无开销的方式传输的辅助文本信息，使解码器能够利用这种语义指导。 我们的方法TextBoost通过三个战略设计将这一理念付诸实践：(i) 自适应地过滤OCR输出，并将其转换为引导图；(ii) 通过注意力引导融合块以校准方式整合该引导与解码器特征；(iii) 在文本区域强制执行一致性重建，同时采用正则化损失来促进自然的场景融合。在TextOCR和ICDAR 2015数据集上的广泛实验表明，相较于同等峰值信噪比（PSNR）和每像素比特数（bpp），TextBoost可以将文本识别F1值提高高达60.6%，同时生成更清晰的小字体文本，并保持全局图像质量，有效地解耦了文本增强与全局率失真优化之间的关系。
URL
https://arxiv.org/abs/2603.04115
