Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach.Comment: 5 Pages, ICIP 202

Adaptive lifting schemes with a global L1 minimization technique for image coding

International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. In this paper, we present a sparse op- timization technique based on recent convex algorithms and applied to the prediction filters of a two-dimensional non separable lifting structure. The idea consists of designing these filters, at each resolution level, by minimizing the sum of the ℓ1-norm of the three detail subbands. Extending this optimization method in order to perform a global minimization over all resolution levels leads to a new opti- mization criterion taking into account linear dependencies between the generated coefficients. Simulations carried out on still images show the benefits which can be drawn from the proposed optimization techniques

Vector Lifting Schemes for Stereo Image Coding

International audienceMany research efforts have been devoted to the improvement of stereo image coding techniques for storage or transmission. In this paper, we are mainly interested in lossyto- lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques. The basic principle involved in this technique first consists of estimating the disparity map. Then, one image is considered as a reference and the other is predicted in order to generate a residual image. In this work, we propose a novel approach, based on Vector Lifting Schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views. We present two versions of this new scheme. A theoretical analysis of the performance of the considered VLS is also conducted. Experimental results indicate a significant improvement using the proposed structures compared with conventional methods

Two-dimensional non separable adaptive lifting scheme for still and stereo image coding

International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. However, it has been observed that the separable lifting scheme structure presents some limitations because of the separable processing performed along the image lines and columns. In this paper, we propose to use a 2D non separable lifting scheme decomposition that enables progressive reconstruction and exact decoding of images. More precisely, we focus on the optimization of all the involved decomposition operators. In this respect, we design the prediction filters by minimizing the variance of the detail signals. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artefacts. Simulations carried out on still and stereo images show the benefits which can be drawn from the proposed optimization of the lifting operators

Lifting schemes for joint coding of stereoscopic pairs of satellite images

electronic version (5 pp.)International audienceStereo data compression is an important issue for the new generation of vision systems. In this paper, we are interested in lossless coding methods for stereo images allowing progressive reconstruction. Most of the existing approaches account for the mutual similarities between the left and the right images. More precisely, the disparity compensation process consists in predicting the right image from the left one based on the disparity map. Then, the disparity map, the reference image, and the residual image are encoded. In this work, we propose a novel approach based on the concept of vector lifting scheme. Its main feature is that it does not generate one residual image but two compact multiresolution representations of the left and the right views, driven by the underlying disparity map. Experimental results show a significant improvement using this technique compared with conventional methods

A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment

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

Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos

Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores.Comment: SPIE Medical Imaging 2020 (Draft version

Schémas de lifting vectoriels adaptatifs et applications à la compression d'images stéréoscopiques.

This thesis addresses the problem of stereo image coding for storage and transmission purposes. In particular, we are interested in lossy-to-lossless coding schemes allowing progressive reconstruction. In the first part, we propose novel coding methods based on Vector Lifting Schemes. Unlike conventional approaches which encode a reference image, a residual one and a disparity map, the proposed methods do not generate a residual image but two compact multiresolution representations of the left and the right images simultaneously. We also show the benefits that can be drawn from integrating a smooth and dense disparity field within such joint stereo image coding schemes. In the second part, we propose optimization techniques which can be used in the design of these lifting schemes. This allows us to build content adaptive methods. More precisely, we focus on the optimization of all the operators (i.e the update and prediction filters) involved in a lifting structure. To this end, we propose and analyze a new criterion for the design of the update filter. Concerning the prediction filters, we investigate techniques for optimizing sparsity criteria.L'objectif majeur de ce travail de thèse était de proposer et d'analyser de nouveaux schémas de codage d'images stéréoscopiques. Ces schémas permettent d'assurer la reconstruction progressive avec la possibilité de restitution exacte de la paire d'images. Dans une première partie, nous avons proposé de nouveaux schémas de codage conjoint de la paire d'images stéréo reposant sur le concept du lifting vectoriel. En ce sens, contrairement aux méthodes classiques, la méthode proposée ne génère aucune image résiduelle mais deux représentations multirésolutions compactes de l'image gauche et l'image droite. De plus, nous avons proposé d'intégrer une méthode récente d'estimation de disparité dans des applications de codage d'images stéréoscopiques. Puis, dans une deuxième partie, nous nous sommes intéressés aux schémas de lifting 2D non séparables tout en nous focalisant sur les aspects d'optimisation des différents filtres mis en jeu. La structure de lifting 2D considérée est composée de trois étapes de prédiction suivies par une étape de mise à jour. Plus précisément, nous avons proposé et analysé une nouvelle méthode pour la conception du filtre de mise à jour. Concernant les filtres de prédiction, nous avons développé de nouveaux critères parcimonieux de types L1