Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder? 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learning or other advanced neural network systems, do have the potential make many existing data compression techniques outdated.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.
you're focused on images so will construct a scenario that fits your question in the affirmative. imagine certain images that are not compressed via lossy compression eg a non jpeg-like algorithm. imagine the images are not compressed and stored in a straight pixel format. next suppose there are images that are composed of sub images in a vertical fashion. eg imagine a stack of images composing a larger image:

A
B
C
D


in the above crude diagram A,B,C,D are stacked images that compose a larger image, say E. now suppose that one creates a file that is concatenating files/images A,B,C,D,E. on a standard "lossless" dictionary compression algorithm like lempel-ziv, the algorithm will be smart enough to use A,B,C,D as long dictionary strings, and compose E out of those strings. (this depends on specifics like maximal dictionary word size etc, but it could be made to work). so in this admittedly rather contrived scenario, the answer is YES.

however, you are missing something about JPEG, it is typically lossy compression, and also images are generally a 2d format, whereas a lot of compression algorithms take advantage of 1d patterns like long strings. so this lossiness combined with the 2d format will mean that general compression algorithms will not really save much over the cost to store the original images (which generally are already compressed anyway). 

therefore this is generally a negative answer overall, the overall idea of "compressing" using a "dictionary of images" is not really valid/possible/plausible and you can get a better idea of why by studying the mechanics/dynamics of compression algorithms in a more detailed way. on the other hand there are a wide variety of image compression algorithms that do in fact take advantage of redundancies in images. in JPEG for example the image is broken into squares and it can reuse common squares!