Stall-Free Software–Hardware Co-Design for Perception-Driven Contrast-Limited Adaptive Histogram Equalization

Abstract

Contrast-Limited Adaptive Histogram Equalization (CLAHE) is widely used in industrial and embedded imaging, yet a fixed Clip Limit often fails to balance detail enhancement and noise suppression across diverse scenes while maintaining stall-free, one-pixel-per-cycle pixel-stream throughput. This paper proposes a deployment-driven, perception-guided software–hardware co-design for CLAHE, where perceptual guidance from offline no-reference image quality assessment (NR-IQA) signals is distilled into a low-dimensional policy that only consumes hardware-observable statistics, and the field-programmable gate array (FPGA) architecture embeds closed-loop control without stalling the main pixel path. On the learning side (offline, in software), Clip Limit tuning is formulated as a Markov decision process (MDP) and learned with Soft Actor-Critic (SAC) using a dual-teacher distillation scheme that transfers privileged NR-IQA guidance to a low-dimensional student policy. Posterior trajectory selection (PTS) and Q4.12 quantization-aware distillation further improve robustness under partial observability and fixed-point inference. On the hardware side, we implement an FPGA single-clock-domain streaming pipeline that integrates feature extraction, student inference, and CLAHE mapping while preserving one-pixel-per-cycle throughput, and we reduce critical-path complexity via division elimination and separable fixed-point interpolation. The controller performs one update per frame and commits the new Clip Limit at the frame boundary; therefore, a five-step horizon corresponds to a five-frame gradual adaptation with intra-frame consistency. Experiments show that the proposed controller is competitive with strong fixed-parameter baselines in mean NR-IQA scores, while reducing failure-sensitive upper-tail degradation using hardware-observable inputs only.