An adaptive signal compression system with pre-specified reconstruction quality and compression rate

Area of Science:

• Computer Science
• Signal Processing
• Data Compression

Background:

• Signal compression methods are evaluated by compression rate and reconstruction quality.
• Existing adaptive signal compression (ACS) systems define performance using complexity, sensitivity, and data size.
• Optimizing compression performance requires expressing it as a function of system complexity and sensitivity.

Purpose of the Study:

• To modify the adaptive signal compression (ACS) system to estimate reconstruction quality.
• To establish a relationship between reconstruction quality and system parameters (complexity, sensitivity).
• To enable optimization of compression rate or reconstruction quality based on system complexity and sensitivity.

Main Methods:

• Modification of the adaptive signal compression (ACS) system into a modified ACS (MACS).
• Developing a model within MACS to estimate reconstruction quality.
• Analyzing the relationship between system complexity, sensitivity, and reconstruction quality.

Main Results:

• The modified ACS (MACS) can estimate reconstruction quality for given system complexity and sensitivity.
• A clear relationship is identified between system parameters and reconstruction quality.
• The system allows for optimizing either compression rate or reconstruction quality.

Conclusions:

• The modified ACS (MACS) provides a method to predict and optimize signal compression performance.
• This approach allows users to determine desired reconstruction quality upfront.
• It facilitates balancing compression rate and reconstruction quality by adjusting system complexity and sensitivity.