Exploring Resolution Fields for Scalable Image Compression with
  Uncertainty Guidance
Dongyi Zhang Feng Li Man Liu Runmin Cong Huihui Bai Meng Wang Yao Zhao
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.