Images also have to get to users really quickly since people typically refuse to wait for more than a second for a web page to load. In addition to that, images have to be sent over the internet in huge batches (think of an Instagram feed or a Facebook page full of vacation pics). Each of these pictures takes up an immense amount of space (see the quantitative estimate below). The internet is full of pictures, from low-resolution user avatars all the way through 8K resolution computer desktop wallpapers. The primary reason for studying the compression of image data is that there is a lot of it. In this article, we describe the ways of compressing image data in particular–starting from the traditional algorithmic approaches and building towards fancy new developments in the field of machine learning.īefore we spend time learning “how?”, however, we should answer the “why?” Table of Contents Many general algorithms that can be applied to any data whatsoever are known for this task, but specialized approaches allow for incredible efficiency for specific types of data. It is basically a way to trade computational resources for the decrease in the sheer quantity of data stored.
Recently, an ever-growing share of the world’s computers works with Big Data, a marketing term roughly meaning “a lot more data than you think.” This means that computer storage efficiency, despite all the advances in the field, still requires incredible ingenuity.ĭata compression is a large part of the problem of storage efficiency.
Even though computers have been rapidly getting better, the tasks we run on computers have also been getting harder and harder.
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