DCT is the most widely used lossy compression method, and is used in multimedia formats for images (such as JPEG and HEIF), video (such as MPEG, AVC and HEVC) and audio (such as MP3, AAC and Vorbis). Lossy image compression is used in digital cameras, to increase storage capacities.

There are two main types of compression: lossy and lossless.

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. Methods for lossless image compression are: Run-length encoding. Entropy coding.

A common data compression technique removes and replaces repetitive data elements and symbols to reduce the data size. Data compression for graphical data can be lossless compression or lossy compression, where the former saves all replaces but save all repetitive data and the latter deletes all repetitive data.

Data Compression is a technique in which the size of data is reduced without loss of information. Lossy compression and Lossless compression are the categories of data compression method.

Data Compression Techniques Mainy all the techniques are divided in two basic types. Lossless Compression Lossy Compression 11. Lossless Compression 12. Lossless Compression Technique As per its name, No data loss. reconstruct the original message exactly from the compressed message.

In losslessdata compression, the integrity of the data is preserved. The original data and the data after compression and decompression are exactly the same because, in these methods, the compression and decompression algorithms are exact inverses of each other: no part of the data is lost in the process.

Lossy Compression Unlike Lossless, this method reduces data by eliminating specific information. It can achieve very high compression ratios through data removal. It could happen that if user try to decompress it, only a part of the original information is still there.

Binary data M we want to compress. Encode. Generate a “compressed” representation C(M). Decode. Reconstruct original message or some approximation M’. Compression ratio. Bits in C(M) / bits in M. Lossless. M = M’, 50-75% or lower. Ex. Natural language, source code, executables.