Image Compression

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unaccceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.

There are several different ways in which image files can be compressed. For Internet use, the two most common compressed graphic image formats are the JPEG format and the GIF format. The JPEG method is more often used for photographs, while the GIF method is commonly used for line art and other images in which geometric shapes are relatively simple.

Other techniques for image compression include the use of fractals and wavelets. These methods have not gained widespread acceptance for use on the Internet as of this writing. However, both methods offer promise because they offer higher compression ratios than the JPEG or GIF methods for some types of images. Another new method that may in time replace the GIF format is the PNG format.

A text file or program can be compressed without the introduction of errors, but only up to a certain extent. This is called lossless compression. Beyond this point, errors are introduced. In text and program files, it is crucial that compression be lossless because a single error can seriously damage the meaning of a text file, or cause a program not to run. In image compression, a small loss in quality is usually not noticeable. There is no "critical point" up to which compression works perfectly, but beyond which it becomes impossible. When there is some tolerance for loss, the compression factor can be greater than it can when there is no loss tolerance. For this reason, graphic images can be compressed more than text files or programs.

Image compression is the application of data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.

Image compression can be lossy or lossless. Lossless compression is sometimes preferred for artificial images such as technical drawings, icons or comics. This is because lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossless compression methods may also be preferred for high value content, such as medical imagery or image scans made for archival purposes. Lossy methods are especially suitable for natural images such as photos in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces imperceptible differences can be called visually lossless.

Methods for lossless image compression are:

• Run-length encoding – used as default method in PCX and as one of possible in BMP, TGA, TIFF
• DPCM and Predictive Coding
• Entropy encoding
• Adaptive dictionary algorithms such as LZW – used in GIF and TIFF
• Deflation – used in PNG, MNG and TIFF
• Chain codes

Methods for lossy compression:

• Reducing the color space to the most common colors in the image. The selected colors are specified in the color palette in the header of the compressed image. Each pixel just references the index of a color in the color palette. This method can be combined with dithering to avoid posterization.
• Chroma subsampling. This takes advantage of the fact that the eye perceives spatial changes of brightness more sharply than those of color, by averaging or dropping some of the chrominance information in the image.
• Transform coding. This is the most commonly used method. A Fourier-related transform such as DCT or the wavelet transform are applied, followed by quantization and entropy coding.
• Fractal compression.

The best image quality at a given bit-rate (or compression rate) is the main goal of image compression. However, there are other important properties of image compression schemes:

Scalability generally refers to a quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression). Other names for scalability are progressive coding or embedded bitstreams. Despite its contrary nature, scalability can also be found in lossless codecs, usually in form of coarse-to-fine pixel scans. Scalability is especially useful for previewing images while downloading them (e.g. in a web browser) or for providing variable quality access to e.g. databases. There are several types of scalability:

• Quality progressive or layer progressive: The bitstream successively refines the reconstructed image.
• Resolution progressive: First encode a lower image resolution; then encode the difference to higher resolutions.
• Component progressive: First encode grey; then color.

Region of interest coding. Certain parts of the image are encoded with higher quality than others. This can be combined with scalability (encode these parts first, others later).

Meta information. Compressed data can contain information about the image which can be used to categorize, search or browse images. Such information can include color and texture statistics, small preview images and author/copyright information.

Processing power. Compression algorithms require different amounts of processing power to encode and decode. Some high compression algorithms require high processing power.

The quality of a compression method is often measured by the Peak signal-to-noise ratio. It measures the amount of noise introduced through a lossy compression of the image. However, the subjective judgement of the viewer is also regarded as an important, perhaps the most important, measure.

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