Continuous Patch Stitching for Block-wise Image Compression
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Most recently, learned image compression methods have outpaced traditional
hand-crafted standard codecs. However, their inference typically requires to
input the whole image at the cost of heavy computing resources, especially for
high-resolution image compression; otherwise, the block artefact can exist when
compressed by blocks within existing learned image compression methods. To
address this issue, we propose a novel continuous patch stitching (CPS)
framework for block-wise image compression that is able to achieve seamlessly
patch stitching and mathematically eliminate block artefact, thus capable of
significantly reducing the required computing resources when compressing
images. More specifically, the proposed CPS framework is achieved by
padding-free operations throughout, with a newly established parallel
overlapping stitching strategy to provide a general upper bound for ensuring
the continuity. Upon this, we further propose functional residual blocks with
even-sized kernels to achieve down-sampling and up-sampling, together with
bottleneck residual blocks retaining feature size to increase network depth.
Experimental results demonstrate that our CPS framework achieves the
state-of-the-art performance against existing baselines, whilst requiring less
than half of computing resources of existing models. Our code shall be released
upon acceptance.
