By losing some information, lossy compression algorithms can achieve a better compression ratio. Luckily, most of the information we lose tends to be information that the human eye can’t detect. In image compression, we can either conserve all information from an image with a lossless algorithm such as PNG, or we can lose some information about the image with a lossy algorithm such as JPEG. Images online are usually in PNG or JPEG format. Overview of Image Compressionīefore going into the bells and whistles of convolutional neural networks-how does image compression work in general? Basically, image compression is very often simply a business necessity. Facebook would not have many users without image compression, and its requirements for image loading are much tamer in comparison to power-users like Instagram. This might not seem like that long, but realistically this is a ridiculously long time for a single small webpage to load. Ignoring metadata, the size of the contents (in bytes) can be computed as follows: size = width × height × 3 (RGB) \text 8. Now, let’s do some napkin mathematics to estimate the size of a small (by modern standards) image file, to further drive home the point of image data demanding compression. This means that an algorithm that can imperfectly compress images has a very real chance of replacing traditional general-case techniques (this is exactly what we see in practice with e.g., JPEG). People are also really good at filtering out small amounts of noise from images, etc. For example, humans are really bad at determining colors (except in relation to each other), so small errors in the color content of the image are likely to go unnoticed. To demonstrate that, we note once again that domain-specific compression performs a lot better than generic compression, and also that images are actually highly resistant to artifacts. Finally, video files are basically made up of thousands of images that have to be sent over the web in real-time, and image compression can help with that (although this task is so demanding that algorithms usually have to be adapted to exploit the time-wise properties of video).Īll this makes for a pretty compelling reason to compress images, but not for why image compression deserves to be its own field. 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.
#Real time image compression software full
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.