A Novel Quantised Image Sensing for Machine Vision

ABSTRACT This paper proposes a quantisation‐driven data‐compression framework for machine vision sensors by combining nonlinear quantisation and bit‐shift masking. The proposed approach compresses pixel data to an effective 1–4 bits per pixel while preserving edge‐salient information required for object detection and feature tracking. Experimental results show that the compressed/quantised images maintain detection and tracking performance without degradation, indicating the proposed framework can reduce sensor‐to‐processor bandwidth and optimise required bit resolution.