BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues
Quality assessment of laparoscopic videos is critical for ensuring accurate diagnostics and surgical precision. Traditional quality assessment methods typically focus on either spatial or textural features independently, limiting their effectiveness in handling composite distortions like motion blur...
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Elsevier
2025-11-01
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| Series: | Biomedical Engineering Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667099225000404 |
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| author | Sria Biswas Rohini Palanisamy |
| author_facet | Sria Biswas Rohini Palanisamy |
| author_sort | Sria Biswas |
| collection | DOAJ |
| description | Quality assessment of laparoscopic videos is critical for ensuring accurate diagnostics and surgical precision. Traditional quality assessment methods typically focus on either spatial or textural features independently, limiting their effectiveness in handling composite distortions like motion blur, noise, defocus blur, uneven illumination, and smoke. To address this, leveraging spatio-temporal interdependencies and textural features offers a more comprehensive approach in replicating the human visual system to improve the robustness of video quality assessment. This work introduces Blind Laparoscopic Video Quality Evaluator (BLVQE) that models the statistical interdependencies between spatial, temporal and texture features. For this, laparoscopic videos obtained from a public database are used to estimate the Luminance and motion vector maps, which are then analyzed using bivariate generalized Gaussian distribution to capture spatio-temporal interdependency. Scene texture complexity is further quantified using statistical energy measures. These feature vectors are used for end-to-end training of an LSTM framework for frame quality predictions. The training and validation loss curves of the model saturate around 50 epochs, indicating prediction proficiency. BLVQE predictions show a high correlation with subjective scores exhibiting robust and competitive performance against other state-of-the-art methods. Ablation studies highlight the contribution of individual feature elements, confirming the superiority of the selected features. These findings enhance the understanding of the spatial, temporal and textural variations that influence video quality and highlight the potential of joint dependencies in accurately estimating the diagnostic quality of laparoscopic videos. |
| format | Article |
| id | doaj-art-613e615b60514f8b9b548cff2bd813c6 |
| institution | Kabale University |
| issn | 2667-0992 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomedical Engineering Advances |
| spelling | doaj-art-613e615b60514f8b9b548cff2bd813c62025-08-20T03:27:02ZengElsevierBiomedical Engineering Advances2667-09922025-11-011010018410.1016/j.bea.2025.100184BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cuesSria Biswas0Rohini Palanisamy1Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, Tamil Nadu, 600127, IndiaCorresponding author.; Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, Tamil Nadu, 600127, IndiaQuality assessment of laparoscopic videos is critical for ensuring accurate diagnostics and surgical precision. Traditional quality assessment methods typically focus on either spatial or textural features independently, limiting their effectiveness in handling composite distortions like motion blur, noise, defocus blur, uneven illumination, and smoke. To address this, leveraging spatio-temporal interdependencies and textural features offers a more comprehensive approach in replicating the human visual system to improve the robustness of video quality assessment. This work introduces Blind Laparoscopic Video Quality Evaluator (BLVQE) that models the statistical interdependencies between spatial, temporal and texture features. For this, laparoscopic videos obtained from a public database are used to estimate the Luminance and motion vector maps, which are then analyzed using bivariate generalized Gaussian distribution to capture spatio-temporal interdependency. Scene texture complexity is further quantified using statistical energy measures. These feature vectors are used for end-to-end training of an LSTM framework for frame quality predictions. The training and validation loss curves of the model saturate around 50 epochs, indicating prediction proficiency. BLVQE predictions show a high correlation with subjective scores exhibiting robust and competitive performance against other state-of-the-art methods. Ablation studies highlight the contribution of individual feature elements, confirming the superiority of the selected features. These findings enhance the understanding of the spatial, temporal and textural variations that influence video quality and highlight the potential of joint dependencies in accurately estimating the diagnostic quality of laparoscopic videos.http://www.sciencedirect.com/science/article/pii/S2667099225000404Blind Laparoscopic Video Quality AssessmentFeature interdependencyLong short-term memoryNo referenceTexture energy |
| spellingShingle | Sria Biswas Rohini Palanisamy BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues Biomedical Engineering Advances Blind Laparoscopic Video Quality Assessment Feature interdependency Long short-term memory No reference Texture energy |
| title | BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues |
| title_full | BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues |
| title_fullStr | BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues |
| title_full_unstemmed | BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues |
| title_short | BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues |
| title_sort | blvqe blind laparoscopic video quality evaluator using spatio temporal interdependency and textural cues |
| topic | Blind Laparoscopic Video Quality Assessment Feature interdependency Long short-term memory No reference Texture energy |
| url | http://www.sciencedirect.com/science/article/pii/S2667099225000404 |
| work_keys_str_mv | AT sriabiswas blvqeblindlaparoscopicvideoqualityevaluatorusingspatiotemporalinterdependencyandtexturalcues AT rohinipalanisamy blvqeblindlaparoscopicvideoqualityevaluatorusingspatiotemporalinterdependencyandtexturalcues |