CBANet: Toward Complexity and Bitrate Adaptive Deep Image Compression Using a Single Network.

Area of Science:

• Computer Vision
• Machine Learning
• Signal Processing

Background:

• Current deep image compression methods primarily focus on the rate-distortion trade-off.
• Existing frameworks often lack adaptability to varying computational complexity levels.
• There is a need for a unified approach to manage rate, distortion, and complexity simultaneously.

Purpose of the Study:

• To propose a novel deep image compression framework, CBANet, capable of supporting variable bitrates and computational complexity levels within a single network.
• To address the complex optimization problem of balancing rate, distortion, and computational complexity.
• To introduce a generalizable network design strategy for adaptive image compression.

Main Methods:

• Developed the Complexity and Bitrate Adaptive Network (CBANet) framework.
• Proposed a two-step approach to decouple the optimization into complexity-distortion and rate-distortion sub-tasks.
• Introduced a Complexity Adaptive Module (CAM) and a Bitrate Adaptive Module (BAM) for adaptive trade-offs.

Main Results:

• CBANet effectively learns a single network for variable bitrate and complexity levels.
• The proposed CAM and BAM modules successfully achieve complexity-distortion and rate-distortion trade-offs.
• Experiments on benchmark datasets validate the framework's effectiveness for deep image compression.

Conclusions:

• CBANet offers a significant advancement in deep image compression by integrating complexity and bitrate adaptivity.
• The proposed network design strategy is versatile and can be integrated into various deep learning compression methods.
• The framework provides a unified solution for achieving adaptive image compression with a single network.