Compression Improves Image Classification Accuracy

Abstract

We study the relationship between the accuracy of image classification and the level of image compression. Specifically, we look at how various levels of JPEG and SVD compression affect the score of the correct answer in Inception-v3, a TensorFlow-based image classifier trained on the ImageNet database. Surprisingly, the compression seems to improve the ability of Inception-v3 to recognize images, with the best performance seen at fairly high degrees of compression for most images tested (with half achieving maximal score at JPEG quality under 15, corresponding to more than tenfold reduction in file size). The same behaviour holds for images compressed using the singular value decomposition (SVD) method. This phenomenon suggests that even significant compression can be beneficial rather than detrimental to image classification accuracy, in particular for convolutional neural networks. Understanding when and why compression helps, and which compression algorithm and compression ratio are optimal for any given image remains an open problem.