IMAGE COMPRESSION
The Issue of Compression
The storage requirement for uncompressed video is 23.6 megabytes/second (512 pixels * 512 pixels * 3 bytes/pixel * 30 frames/second). With MPEG_1 Compression, full motion video can be compressed down to 187 kilobytes/second at a small sacrifice in quality. Why should we care?
Some of the technological prophets speak of a day when we will rent video electronically. You could call up the local video rental shop, ask for your favorite comedy and magically download it over the phone line. One second of uncompressed video requires 23.6 megabytes of storage. If your comedy is 2 hours long, you will need 169 gigabytes of storage space. Assuming that you have installed a super-bandwidth phone line that can transfer data at 180 megabytes/second, it would take 15.7 minutes to transfer our movie from the video store into our house. That is about one-eighth the time it would take to watch the movie.
If your favorite movie is compressed with MPEG_1, the storage requirements are reduced to 1.3 gigabytes. Using our high bandwidth link, the transfer time would be 7.48 seconds. This is much better.
Clearly, Image Compression is needed. This is apparent by the large number of new hardware and software products dedicated solely to compress images. It is easy to see why CompuServe came up with the GIF file format to compress graphics files. As computer graphics attain higher resolution and Image Processing applications require higher intensity resolution (more bits per pixels), the need for Image Compression will increase. Medical imagery is a prime example of images increasing in both spatial resolution and intensity resolution. Although humans do not need more than 8 bits per pixel to view gray scale images; computer vision can analyze data of much higher intensity resolutions.
Time is money. However, when you consider that the street price for 1M (Mega Bytes) of RAM (Random Access Memory) is about $ 25 dollars, and the street price for hard disk drives storage hovers around $2 dollars per megabyte, you can say " Storage is Money " if you are going to deal with images in the 20M range (which is common in the publishing world), it would cost you atleast $500 dollars for the RAM chips alone to work with one image. For the hard drive on which you would store such images, with a capacity around 1000M, you will easily pay over $1000 dollars!
By using Compressions techniques, you can dramatically cut these large image files down to size. For Example, an image that requires 9,21,000 bytes of storage space when uncompressed, needs only 1,00,000 bytes of storage after Compression. That's a Compression factor of 90%, or a Compression ratio of 9 to 1 (9:1)! The original can be reconstructed identically from the Compressed Version.
Some formats even enable you to reach higher Compression levels around 22:1 range. That would bring your 9,21,000 byte file down to only 42,000 bytes. Compression ratios this high usually requires that unnecessary data be thrown away, thus simplifying the image and reducing its size. We call these Compression methods Lossy because they lose some of the data during Compression Process.
Compression ratios are commonly presenting discussions of data Compression. A Compression Ratio is simply the size of the original data divided by the size of the compressed data. A technique that compresses a 1 megabyte image to 100 kilobytes has achieved a Compression Ratio of 10.
Compression Ratio = Original data / Compressed data
= 1 M bytes / 100 K bytes
= 10.0
For a given image, the greater the Compression Ratio, the smaller the final image will be.
An example which shows Image Compression on an Image using Run Length Encoding
An Image Showing Defects on Steel Strips
HOW COMPRESSION WORKS:
Most Compression methods are fairly simple and many have been used for centuries. Language itself is an example of Compression. The word mountain brings to your mind a wealth of information. Perhaps you think of a large Snow covered Peak, or may be a beautiful Mountain Range. If you recorded digitally all the information that comes to your mind with the single word Mountain, it would require a massive amount of storage.
The saying" ...one by the land and two if by sea ... " is an example of Compression. The symbols one and two represent larger pieces of data. Even television uses a form of Compression with the images it brings to our homes.
There are two basic type of Image Compression: Lossless Compression and Lossy Compression. A Lossless scheme encodes and decodes the data perfectly, and the resulting image matches the original image exactly. There is no degradation in the process--- no data is lost.
Lossy Compression allows redundant and non-essential information to be lost. Typically with lossy scheme there is a trade-off between Compression and image quality. You are able to compress an image down to an incredibly small size but looks so poor that it is not worth the trouble. Though not always the case, Lossy Compression techniques are typically more complex and require more computations.
Lossy Image Compression schemes remove data from the image that the human eye would not notice. This works well for images that are meant to be viewed by humans. If the image is to be analyzed by a machine, Lossy Compression schemes may not be appropriate. The computer will clearly detect the information loss that the human eye may not. The goal of Lossy Compression is that the final decompressed image be virtually lossless. Hopefully, the information removed from the image goes unnoticed by the human eye.
Many people associate huge degradations with Lossy Image Compression. What they don't realize is that most of the degeneration are small if even noticeable. The entire imaging operation is Lossy, scanning or digitizing the image is a Lossy process, and displaying an image on the screen or printing the hard copy is Lossy. The goal is to keep the losses indistinguishable.
Which Compression Technique to use depends on the Image Data. Some images especially those used for medical diagnosis, cannot afford to lose any data. A Lossless Compression scheme will need to be used. Computer generated graphics with large areas of the same color compress well with simple Lossless Scheme like Run Length Encoding or LZW. Continuous tone images with complex shapes and shading will require a Lossy Compression technique to achieve high Compression Ratio. Images with high degree of detail that can not be lost, such as detailed CAD drawings, cannot be compressed with Lossy Compressions.
When choosing a Compression technique, you must look at more than the achievable Compression Ratio. The Compression Ratio alone tells you nothing about the quality of the resulting image. Other things to consider are the Compression / Decompression Time, Algorithm complexity, cost and availability of computational resources, and how standardized the technique is. If you use a Compression method that achieves fantastic Compression Ratios but you are the only one using it, you will be limited in your applications. If your images need to be viewed by any hospital in the world, you better use standardized Compression technique and file format.
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