JPEG to Compress Vector Graphics?

Compressing, entropy and information theory have things in common with economics. Nobody’s going to turn his head unless you stop talking maths. I truly understand they are a bit boring and complex for your grandmother, but surprisingly most of the technical people doesn’t bother to understand the underlying concept either. In this post, I’m going to pass over the following topics in a daily tongue to illustrate the overall scheme on your mind:

1. What is image compression and why do we use it?
2. A short brief of JPEG compression.
3. A review of Google Maps and Live Search Maps for serving images as the primary content.

Introduction to Image Compression

If you understand the term "compression", it doesn’t mean we are over with the definition. Hold on.

Image compression, the art and science of reducing the amount of data required to represent an image, is one of the most useful and commercially successful technologies in the field of digital image processing. (Digital Image Processing 3rd Ed., Gonzalez & Woods, page 525)

Let’s first try to understand how compression became one of the most commercially successful field in image processing? With the irrepressible popularity of television and Internet (after mid-90s), images and videos become significant elements to represent information. With no compression;  a coloured standard TV broadcast, 640×480 wide with refresh rate of 30 frames per seconds, requires 27,648,000 bytes to be transmitted per second. Even in tomorrow’s technology, supplying a connection of almost 30Mbytes/second just for a TV cast doesn’t seem to be possible with no doubt. It’s no surprise to hear many failed quotes back from early 1900s that television will never be able to find opportunity to be on the market.

Data versus Information

When the issue is compression, it refers to the compression of data. Data is being transferred to carry information. Therefore, we might be able to reduce the amount of data to represent a given quantity of information. A parrot in a very populated barber shop in downtown loves to say "Hello" to every new customer that comes in. How would you transmit the words it spells in text most efficiently?

Statistically hello is the most common word. Representing it in a bit is highly acceptable, instead of transferring 5 characters (5*8 = 40 bits). In the example above, it’s very clear that statistics say "stranger" is the second word we most likely to hear from parrot and so on. Converting (mapping) the string array into a bit stream saves 94% of bandwidth in this case. Huffman coding, which is going to remind you the method above, guarantees you to use minimum possible number of bits if you have statistical information of data.

Image Compression Techniques

Generally, image compression techniques are separated into two columns:  lossy and lossless compression. Lossy methods takes the advantage of capabilities of human vision range, eliminates details and loose information to reduce amount of data. Lossy methods are mostly used for natural images. In lossless compression, encoding process finds a smart way to represent same amount of information in lesser amount of data, just like in the example above with Mr. Parrot. And some may use hybrid models to mix advantages of both sides. Read the rest of this entry »