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Title: CARP: Compression Through Adaptive Recursive Partitioning for Multi-Dimensional Images
Fast and effective image compression for multi- dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high- resolution images and videos. Desirable properties in com- pression methods include (1) high reconstruction quality at a wide range of compression rates while preserving key local details, (2) computational scalability, (3) applicability to a variety of different image/video types and of different dimen- sions, and (4) ease of tuning. We present such a method for multi-dimensional image compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a par- simonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve self-tuning and regularization to avoid overfitting—resulting in one single parameter to be specified by the user to achieve the desired compression rate. Extensive numerical experi- ments using a variety of datasets including 2D ImageNet, 3D medical image, and real-life YouTube and surveillance videos show that CARP dominates the state-of-the-art com- pression approaches—including JPEG, JPEG2000, MPEG4, and a neural network-based method—for all of these dif- ferent image types and often on nearly all of the individual images.  more » « less
Award ID(s):
1749789
PAR ID:
10167917
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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