Michela Testolina

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Michela Testolina obtained her B.Sc. and M.Sc. in Telecommunication Engineering from the University of Trento, Italy. From 2020 she is a PhD student at the Multimedia Signal Processing Group (MMSPG) under the supervision of Prof. Touradj Ebrahimi. Her main research interests are in the field of image processing, in particular image compression and quality metrics.

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The use of point clouds to digitally represent three-dimensional objects with both geometry and color attributes is rapidly increasing in several applications. Since storage and transmission of uncompressed point cloud data are often impractical, several lossy compression algorithms have been proposed, each exhibiting specific types of visual distortion. This creates a challenging environment for objective quality metrics, which might be effective only on a restricted number of distortion types and contexts. To evaluate the performance of the most recent objective quality metrics in predicting distortions as perceived by humans, a benchmarking study is conducted using subjective scores from observers examining models distorted with a conventional as well as a learning-based compression method, while rendering them on both a traditional flat monitor and an eye-sensing light field display. The results are then analyzed and conclusions are drawn on the correlation between recent state-of-the-art objective quality metrics and human perception on two different types of visualization devices.

IEEE2022

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On combining denoising with learning-based image decoding

Touradj Ebrahimi, Michela Testolina

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captured images. More recently, learning-based solutions have shown impressive results in image enhancement in general, and in image denoising in particular. In this paper, we review multiple novel solutions for image denoising in the compressed domain, by integrating denoising operations into the decoder of a learning-based compression method. The paper starts by explaining the advantages of such an approach from different points of view. We then describe the proposed solutions, including both blind and non-blind methods, comparing them to state of the art methods. Finally, conclusions are drawn from the obtained results, summarizing the advantages and drawbacks of each method.

SPIE2022

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2022

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