Attention-driven low-cost image contrast enhancement for low-power OLED display

Area of Science:

• Computer Vision
• Image Processing
• Display Technology

Background:

• Organic light-emitting diode (OLED) displays require substantial computational resources for high-resolution content processing.
• Current methods often lack power optimization, leading to increased energy consumption.

Purpose of the Study:

• To develop a power-aware visual contrast enhancement (PAVE) method for OLED displays.
• To reduce computational cost and optimize power usage for high-resolution image processing.

Main Methods:

• Utilizes original and low-resolution images for multilevel feature extraction.
• Incorporates a power-attention mechanism, fast-guided filter (GF), and local tone mapping.
• Focuses on preserving on-demand power and minimizing computational load.

Main Results:

• Achieves an optimized image quality with a Structural Similarity Index Measure (SSIM) of 0.98%.
• Demonstrates a significantly reduced CPU time of 0.05 seconds for 2K resolution images.
• Effectively balances image enhancement with power efficiency.

Conclusions:

• The proposed PAVE method offers an efficient solution for processing high-resolution images on OLED displays.
• PAVE successfully reduces computational complexity and optimizes power consumption without compromising visual quality.
• This approach is crucial for developing energy-efficient display technologies.