The objective of image compression is to reduce irrelevance and redundancy of the
image data in order to be able to store or transmit data in an efficient form.
Lossy and lossless Image compression Image compression may be lossy or
lossless. Lossless compression is preferred for archival purposes and
often for medical imaging, technical drawings, clip art, or comics. Lossy
compression methods, especially when used at low bit rates, introduce
compression artifacts. Lossy methods are especially suitable for natural images
such as photographs in applications where minor loss of fidelity is
acceptable to achieve a substantial reduction in bit rate. The lossy
compression that produces imperceptible differences may 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
Area image compression 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 human 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. In particular, a
Fourier-related transform such as the Discrete Cosine Transform is widely
used: N. Ahmed, T. Natarajan and K.R.Rao, "Discrete Cosine Transform,"
IEEE Trans. Computers, 90-93, Jan. 1974. The DCT is sometimes referred to as
"DCT-II" in the context of a family of discrete cosine transforms; e.g., see
discrete cosine transform. The more recently developed wavelet transform is
also used extensively, followed by quantization and entropy coding.
Fractal compression. Other properties
The best image quality at a given bit-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.
Other names for scalability are progressive coding or embedded
bitstreams. Despite its contrary nature, scalability also may be found in
lossless codecs, usually in form of coarse-to-fine pixel scans. Scalability
is especially useful for previewing images while downloading them 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 may be
combined with scalability. Meta information. Compressed data may
contain information about the image which may be used to categorize, search,
or browse images. Such information may include color and texture statistics,
small preview images, and author or 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
often is 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 judgment of the viewer also is regarded as an important
measure, perhaps, being the most important measure.
Notes and references External links
Image compression from MIT OpenCourseWare
Image Coding Fundamentals A study about image compression
Data Compression Basics FAQ:What is the state of the art in
lossless image compression? from comp.compression
IPRG Open group related to image processing research resources