Image compression with discriminative dictionaries

Common image compression algorithms like JPEG or JPEG2000 transform the individual pixel values into a domain that favors a compact representation. In contrast to the fixed DCT or Wavelet domains, recent efforts were made on image coding with learned overcomplete dictionaries. In this work, we investigate the question whether dictionaries based on classification features are usable for image compression. We show that, despite their original purpose is to extract discriminative features within a Convolutional Neural Network, these features are capable of reaching competitive compression results when combined with a sparsity promoting coding scheme.