探索用于可扩展图像压缩的分辨率场与不确定性引导
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance
AI总结：
针对现有学习式图像压缩灵活性不足的问题，提出融合分辨率场与不确定性引导的互逆金字塔网络RPN，通过跨分辨率上下文挖掘、渐进码流复用及反向不确定性优化，实现高性能的空间与质量可扩展图像压缩。
摘要：
近年来，基于学习的图像压缩方法取得了显著进展，性能超越了传统编码标准。这类方法大多优先在特定压缩率下实现最优率失真性能，这限制了其在约束复杂多变的各类应用中的灵活性与适配性。本研究探索了分辨率场在可扩展图像压缩中的潜力，提出了互逆金字塔网络（reciprocal pyramid network，RPN），以满足更具适配性和通用性的压缩需求。具体而言，RPN首先构建压缩金字塔，并以自顶向下的方式生成不同层级的分辨率场。其核心设计是相邻层级间的跨分辨率上下文挖掘模块，该模块执行特征增强与蒸馏，挖掘有意义的上下文信息并去除不必要的冗余，生成富含信息的分辨率场作为残差先验。可扩展性通过渐进式码流复用以及随层级变化的分辨率场融合实现。此外，在相邻压缩层级之间，我们显式量化来自底层解码表征的偶然不确定性（aleatoric uncertainty），并设计了不确定性引导损失来更新上层压缩参数，形成一个反向金字塔过程，促使网络聚焦于高方差的纹理像素，以实现更可靠、更准确的重建。通过以金字塔结构结合分辨率场探索与不确定性引导，RPN可有效实现空间和质量可扩展的图像压缩。实验表明，RPN的性能优于现有的经典可扩展编解码器以及基于深度学习的可扩展编解码器。代码将发布于https://github.com/JGIroro/RPNSIC。
英文摘要：
Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-distortion performance for a particular compression rate, which limits their flexibility and adaptability in various applications with complex and varying constraints. In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression. Specifically, RPN first builds a compression pyramid and generates the resolution fields at different levels in a top-down manner. The key design lies in the cross-resolution context mining module between adjacent levels, which performs feature enriching and distillation to mine meaningful contextualized information and remove unnecessary redundancy, producing informative resolution fields as residual priors. The scalability is achieved by progressive bitstream reusing and resolution field incorporation varying at different levels. Furthermore, between adjacent compression levels, we explicitly quantify the aleatoric uncertainty from the bottom decoded representations and develop an uncertainty-guided loss to update the upper-level compression parameters, forming a reverse pyramid process that enforces the network to focus on the textured pixels with high variance for more reliable and accurate reconstruction. Combining resolution field exploration and uncertainty guidance in a pyramid manner, RPN can effectively achieve spatial and quality scalable image compression. Experiments show the superiority of RPN against existing classical and deep learning-based scalable codecs. Code will be available at https://github.com/JGIroro/RPNSIC.
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