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Explore an algorithmic perspective on realism and perceptual quality in lossy compression through this 55-minute seminar presented by Yassine Hamdi from Imperial College London. Delve into the mathematical and computational approaches used to understand how compression algorithms affect the perceived quality and realism of compressed data. Examine the trade-offs between compression efficiency and perceptual fidelity, and discover how algorithmic techniques can be leveraged to optimize these competing objectives. Learn about the theoretical foundations and practical applications of perceptual quality metrics in compression systems. Gain insights into current research methodologies for evaluating and improving the perceptual performance of lossy compression algorithms. This presentation is part of the "Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning" research programme at the Isaac Newton Institute for Mathematical Sciences.
