DPCM Entropy encoding – the two most common entropy encoding techniques are arithmetic coding and Huffman coding Adaptive dictionary algorithms such as LZW – used in GIF and TIFF DEFLATE – used in PNG, MNG, and TIFF Chain codes Other properties 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: 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 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 (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-scale version; then adding full color. Region of interest coding. Certain parts of the image are encoded with higher quality than others. This may be combined with scalability (encode these parts first, others later). 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. History Entropy coding started in the 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was developed in 1950. 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. An important development in image data compression was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed, T. Natarajan and K. R. Rao in 1973. JPEG was introduced