Efficient in-situ image and video compression through probabilistic image representation

TL;DR

This paper introduces CARP, a probabilistic recursive partitioning method for multi-dimensional image and video compression that outperforms existing standards in quality, scalability, and adaptability.

Contribution

CARP employs a Bayesian hierarchical model for in-situ, self-tuning compression of diverse image/video types with a single parameter, advancing the state-of-the-art.

Findings

CARP surpasses JPEG, JPEG2000, BPG, MPEG4, HEVC, and neural methods in quality.

It effectively compresses 2D images, YouTube videos, and surveillance footage.

The method is scalable, adaptable, and requires minimal user tuning.

Abstract

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 compression 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 dimensions, (4) progressive transmission, and (5) 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 parsimonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP dominates the state-of-the-art image/video compression approaches---including JPEG, JPEG2000, BPG, MPEG4, HEVC and a neural network-based method---for all of these different image types and on nearly all of the individual images and videos over some methods.