If my goal were to compress say 10,000 images and I could include a dictionary or some sort of common database that the compressed data for each image would reference, could I use a large dictionary shared by the entire catalog and therefore get much smaller file sizes?  Could this be expanded to work with images in general, i.e. to replace something like JPEG?Are there existing compression systems that operate like this, where there is a large common set of bits transmitted and loaded before decompression, that has been built by analyzing many images?For example, is there an existing computer science/machine learning research effort using sparse autoencoding over a large set of images and this concept of distributing a network derived from that encoding with the decompressor?Note:  Coming back to this question and the answers, I see that it was primarily not understood because those that read it did not understand enough about new types of machine learning.New techniques in machine learning, particularly deep learning or other advanced neural network systems, do have the potential make many existing data compression techniques outdated.  Or rather, we should expect that some more extensive research will result in these types of powerful encoders.  As of yet this is mainly speculative, and data compression scientists cannot conceive of them without fully understanding the power of the new neural network techniques.
Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder?
machine learning;neural networks;data compression;computer vision