No-Reference Laparoscopic Video Quality Assessment for Sensor Distortions Using Optimized Long Short-Term Memory Framework

Laparoscopic surgery relies on sensor-based video systems vulnerable to visual distortions, requiring rigorous quality checks to meet regulatory standards. This letter introduces a no-reference laparoscopic video quality assessment algorithm designed to replicate human perceptual judgments in the presence of sensor distortions. The method models the statistical interdependencies between luminance and motion features and combines them with texture variations to formulate a perceptually relevant feature vector. This is used as input to train a memory-retentive deep learning model optimized by chaotic maps to predict frame quality scores which are utilized to evaluate the overall video quality. Performance comparisons with state-of-the-art methods show that the proposed model aligns closely with both expert and nonexpert subjective ratings, with experts achieving higher accuracy. Ablation studies further emphasize the effectiveness of the selected feature combinations and regression frameworks, demonstrating the capability of the model to replicate human opinions. These findings highlight the potential of the proposed method as a reliable tool for automating quality assessment in sensor-based laparoscopic systems to ensure high standards in clinical applications.

I. Introduction

Laparoscopic surgeries rely on highly sensitive video imaging sensors to accurately visualize and navigate within the surgical site. These sensors are manufactured to maintain optimal visibility even in the presence of various distortions [1]. These distortions include uneven illumination from fluctuating light sources, blur artifacts from improper focusing, lens fogging caused by tissue cauterization smoke, and noise distortions from channel transmission [2]. To ensure these distortions can be effectively managed in compliance with industry regulatory standards, the sensors undergo rigorous quality checks at every manufacturing stage [3]. Since performing this manually is a time consuming, expensive, and labor-intensive process for large-scale production, there is a high demand for developing laparoscopic video quality assessment (LVQA) algorithms that can produce outputs more closely aligned with human evaluations.