Optimizing JPEG Quantization for Classification Networks

Understand

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.