A new blind image conversion complexity metric for intelligent CMOS image sensors

Abstract

Abstract Many algorithms have been developed for complementary metal–oxide–semiconductor (CMOS) image sensors to speed up analogue‐to‐digital (A‐to‐D) conversion of captured images. However, there is no objective blind‐image quality metric available to compare and quantify the quality and effectiveness of these speed‐up algorithms. In this work, we developed a blind‐image quality and complexity metric for this purpose. The proposed metric relies on counting the successive zeros in a code histogram. The proposed metric is called the conversion complexity metric (CCM). The CCM is designed to quantify how complex, and to predict how much time and power consuming a captured image is for A‐to‐D conversion, mainly by integrating (ramp) type A‐to‐D converter used in column‐parallel architectures of a CMOS image sensor (CIS). The proposed metric, CCM, is tested for linearity, monotonicity, and sensitivity to many types of introduced distortion. The CCM is compared with other no‐reference and full‐reference image quality and complexity metrics. It accomplished, for brightness change distortion, 99% linearity and 316% sensitivity, providing a computationally efficient blind‐image quality metric that no other metrics provide for CIS to intelligently adjust and optimise on‐chip analogue and digital signal processing.

Keywords

• Optical, image and video signal processing
• Signal processing and detection
• Image sensors
• Optimisation techniques
• Computer vision and image processing techniques
• Optimisation techniques