Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models

1 code implementation • 15 Jan 2024 • Dan Jacobellis, Daniel Cummings, Neeraja J. Yadwadkar

Our results indicate three key findings: (1) using generative compression, it is feasible to leverage highly compressed data while incurring a negligible impact on machine perceptual quality; (2) machine perceptual quality correlates strongly with deep similarity metrics, indicating a crucial role of these metrics in the development of machine-oriented codecs; and (3) using lossy compressed datasets, (e. g. ImageNet) for pre-training can lead to counter-intuitive scenarios where lossy compression increases machine perceptual quality rather than degrading it.

Data Compression Image Classification +6