# A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment

arXiv: https://arxiv.org/abs/2202.04517 · HTML: https://mlsanity.com/paper/2202.04517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 64 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos](https://mlsanity.com/paper/1812.01366/) (2018)
- [A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment](https://mlsanity.com/paper/2002.10651/) (2020)
- [KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild](https://mlsanity.com/paper/1912.07966/) (2019)
- [Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos](https://mlsanity.com/paper/2003.12679/) (2020)
- [Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment](https://mlsanity.com/paper/2106.06784/) (2021)
- [Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"](https://mlsanity.com/paper/2005.04400/) (2020)

## Built on by (most cited first, 2 of 2)

- [ExIQA: Explainable Image Quality Assessment Using Distortion Attributes](https://mlsanity.com/paper/2409.06853/) (2024)
- [Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation](https://mlsanity.com/paper/2606.23131/) (2026)

## Abstract (arXiv)

Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.