Ms. Michela Testolina Profile

Michela Testolina

at EPFL

SPIE Involvement:

Author

Proceedings Article | 30 September 2024 Presentation + Paper

On the performance of learning-based image compression as source coding for JPEG DNA

Evgeniy Upenik, Davi Lazzarotto, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 13137, 131370S (2024) https://doi.org/10.1117/12.3031848

KEYWORDS: Image compression, Artificial intelligence, Data storage, Image quality, Image processing

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Proceedings Article | 30 September 2024 Presentation + Paper

On the impact of learning-based image compression on computer vision tasks

Shunsuke Akamatsu, Michela Testolina, Evgeniy Upenik, Touradj Ebrahimi

Proceedings Volume 13137, 131370M (2024) https://doi.org/10.1117/12.3030885

KEYWORDS: Image compression, Artificial intelligence, Image classification, Computer vision technology, Object detection, Image segmentation, Performance modeling, Image processing, Visualization

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Proceedings Article | 30 September 2024 Presentation + Paper

Storing images and point clouds on DNA support with fountain codes

Davi Lazzarotto, Jorge Encinas Ramos, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 13137, 1313711 (2024) https://doi.org/10.1117/12.3030612

KEYWORDS: Image compression, Point clouds, Binary data, Artificial intelligence, Data storage, Forward error correction, Molecules, Image processing, Image quality

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Proceedings Article | 30 September 2024 Presentation + Paper

Benchmarking conventional and learning-based objective image quality metrics on compression artifacts

Michela Testolina, Touradj Ebrahimi

Proceedings Volume 13137, 131370P (2024) https://doi.org/10.1117/12.3030906

KEYWORDS: Image quality, Visualization, Image compression, Visual compression, RGB color model, Image quality standards, Standards development, Visual analytics, Video compression, Video

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Proceedings Article | 4 October 2023 Presentation + Paper

Towards learning-based image compression for storage on DNA support

Sophie Strebel, Noémie Monnier, Davi Lazzarotto, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 12674, 126740V (2023) https://doi.org/10.1117/12.2677356

KEYWORDS: Image compression, Image quality, Data storage, Image storage, Image processing, Molecules

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Proceedings Article | 4 October 2023 Presentation + Paper

On the assessment of high-quality images: advances on the JPEG AIC-3 activity

Michela Testolina, Evgeniy Upenik, Touradj Ebrahimi

Proceedings Volume 12674, 126740Q (2023) https://doi.org/10.1117/12.2677347

KEYWORDS: Image quality, Image compression, Image quality standards, Visual compression, Image processing, Image visualization, Standards development

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Proceedings Article | 7 June 2023 Presentation + Paper

Towards learning-based denoising of light fields

Tomás Soares De Carvalho Feith, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 12571, 1257109 (2023) https://doi.org/10.1117/12.2666000

KEYWORDS: Denoising, Machine learning, Image denoising, Visualization, Image processing, Cameras, 3D modeling

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Proceedings Article | 3 October 2022 Presentation + Paper

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Michela Testolina, Evgeniy Upenik, Jon Sneyer, Touradj Ebrahimi

Proceedings Volume 12226, 122260D (2022) https://doi.org/10.1117/12.2636687

KEYWORDS: Image quality, Image compression, Image quality standards

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Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual artifacts, whose visibility depends on the chosen bitrate. Modern applications target images with high to near-visually lossless quality, in order to maximize the visual quality while still reducing storage space consumption. In this context, subjective and objective image quality assessment are essential tools in order to develop compression methods able to generate images with high visual quality. While a large variety of subjective quality assessment protocols have been standardized in the past, they have been found to be imprecise in the quality interval from high to near-visually lossless. Similarly, an objective quality metric designed to work specifically in the mentioned range has not been designed yet. As current quality assessment methodologies have proven to be unreliable, a renewed activity on the Assessment of Image Coding, also referred to as JPEG AIC, was recently launched by the JPEG Committee. The goal of this activity is to extend previous standardization efforts, i.e. AIC Part 1 and AIC Part 2 (also know as AIC-1 and AIC-2), by developing a new standard, known as AIC Part 3 (or AIC-3). Notably, the goal of the activity is to standardize both subjective and objective visual quality assessment methods, specifically targeting images with quality in the range from high to near-visually lossless. Two Draft Calls for Contributions on Subjective Image Quality Assessment1, 2 were released, aiming at collecting contributions on new methods and best practices for subjective image quality assessment in the target quality range, while a Call for Proposals on Objective Image Quality Assessment is expected to be released at a later date. This paper aims at summarizing past JPEG AIC efforts and reviewing the main objectives of the future activities, outlining the scope of the activity, the main use cases and requirements, and call for contributions. Finally, conclusions on the activity are drawn.