  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.
