  Deep learning for computer vision depends on lossy image compression: it
reduces the storage required for training and test data and lowers transfer
costs in deployment. Mainstream datasets and imaging pipelines all rely on
standard JPEG compression. In JPEG, the degree of quantization of frequency
coefficients controls the lossiness: an 8 by 8 quantization table (Q-table)
decides both the quality of the encoded image and the compression ratio. While
a long history of work has sought better Q-tables, existing work either seeks
to minimize image distortion or to optimize for models of the human visual
system. This work asks whether JPEG Q-tables exist that are "better" for
specific vision networks and can offer better quality--size trade-offs than
ones designed for human perception or minimal distortion. We reconstruct an
ImageNet test set with higher resolution to explore the effect of JPEG
compression under novel Q-tables. We attempt several approaches to tune a
Q-table for a vision task. We find that a simple sorted random sampling method
can exceed the performance of the standard JPEG Q-table. We also use
hyper-parameter tuning techniques including bounded random search, Bayesian
optimization, and composite heuristic optimization methods. The new Q-tables we
obtained can improve the compression rate by 10% to 200% when the accuracy is
fixed, or improve accuracy up to $2\%$ at the same compression rate.

Computer Vision and Pattern Recognition;Graphics;Performance;Image and Video Processing