#IMAGE COMPRESSION FULL#

Transform coding dates back to the late 1960s, with the introduction of fast Fourier transform (FFT) coding in 1968 and the Hadamard transform in 1969. 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.Įntropy coding started in the 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was developed in 1950. The quality of a compression method often is measured by the peak signal-to-noise ratio. Some high compression algorithms require high processing power. Compression algorithms require different amounts of processing power to encode and decode. Such information may include color and texture statistics, small preview images, and author or copyright information. Compressed data may contain information about the image which may be used to categorize, search, or browse images. This may be combined with scalability (encode these parts first, others later). Certain parts of the image are encoded with higher quality than others.

#IMAGE COMPRESSION FULL#

Component progressive: First encode grey-scale version then adding full color.

Resolution progressive: First encode a lower image resolution then encode the difference to higher resolutions.

Quality progressive or layer progressive: The bitstream successively refines the reconstructed image.

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. Despite its contrary nature, scalability also may be found in lossless codecs, usually in form of coarse-to-fine pixel scans. Other names for scalability are progressive coding or embedded bitstreams. Scalability generally refers to a quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression). The best image quality at a given compression rate (or bit rate) is the main goal of image compression, however, there are other important properties of image compression schemes:

Adaptive dictionary algorithms such as LZW – used in GIF and TIFF.

Entropy encoding – the two most common entropy encoding techniques are arithmetic coding and Huffman coding.

Run-length encoding – used in default method in PCX and as one of possible in BMP, TGA, TIFF.

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.

block palette, typically 2 or 4 colors for each block of 4x4 pixels, used in BTC, CCC, S2TC, and S3TC.

Whole-image palette, typically 256 colors, used in GIF and PNG file formats.

This method can be combined with dithering to avoid posterization. Each pixel just references the index of a color in the color palette. The selected colors are specified in the colour palette in the header of the compressed image.

Color quantization - Reducing the color space to a few 'representative' colors in the image.The more recently developed wavelet transform is also used extensively, followed by quantization and entropy coding.DCT is used in JPEG, the most popular lossy format, and the more recent HEIF.It is generally the most efficient form of image compression. The DCT is sometimes referred to as 'DCT-II' in the context of a family of discrete cosine transforms (see discrete cosine transform). It is a type of Fourier-related transform, and was originally developed by Nasir Ahmed, T. Discrete Cosine Transform (DCT) – The most widely used form of lossy compression.Transform coding – This is the most commonly used method.Lossy compression that produces negligible differences may be called visually lossless. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Image compression may be lossy or lossless.