Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression | JoVE Visualize

Area of Science:

Computer Vision
Information Theory
Machine Learning

Background:

Accurate probabilistic modeling of natural images is crucial for efficient image compression.
Current end-to-end image compression methods often simplify entropy modeling by assuming statistically factorized latent codes, which can limit performance.
This assumption of statistical factorization is generally not accurate for natural images.

Purpose of the Study:

To introduce context-based convolutional networks (CCNs) for more effective and efficient entropy modeling in image compression.
To address the limitations of assuming statistically factorized latent codes in existing image compression techniques.
To demonstrate the applicability of CCNs in both lossless and lossy image compression scenarios.

Main Methods:

Developed CCNs incorporating 3D zigzag scanning and 3D code dividing techniques to establish coding contexts for parallel entropy decoding.
Applied CCNs directly to binarized image representations for lossless compression, estimating Bernoulli distributions.
For lossy compression, used three CCNs to estimate parameters of discretized mixture of Gaussian distributions representing categorical distributions of latent codes.
Jointly optimized the CCN-based entropy model with analysis and synthesis transforms for rate-distortion performance.

Main Results:

CCNs demonstrated effective entropy modeling for both lossless and lossy image compression.
Methods utilizing CCNs achieved compression performance comparable to state-of-the-art techniques.
The proposed CCN-based methods were significantly faster than existing state-of-the-art approaches.
Experiments were conducted on standard datasets like Kodak and Tecnick.

Conclusions:

CCNs provide a powerful and efficient approach for entropy modeling in image compression.
The proposed methods overcome the limitations of traditional statistical factorization assumptions.
CCNs offer a promising direction for advancing both lossless and lossy image compression technologies with improved speed and comparable performance.