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In both lossy and lossless compression, information redundancy is reduced, using methods such as coding, quantization, DCT and linear prediction to reduce the amount of information used to represent the uncompressed data. Lossy audio compression algorithms provide higher compression and are used in numerous audio applications including Vorbis ...
An important caveat about lossy compression (formally transcoding), is that editing lossily compressed files causes digital generation loss from the re-encoding. This can be avoided by only producing lossy files from (lossless) originals and only editing (copies of) original files, such as images in raw image format instead of JPEG. If data ...
It is also often used as a component within lossy data compression technologies (e.g. lossless mid/side joint stereo preprocessing by MP3 encoders and other lossy audio encoders). [2] Lossless compression is used in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data ...
In database design, a lossless join decomposition is a decomposition of a relation into relations , such that a natural join of the two smaller relations yields back the original relation. This is central in removing redundancy safely from databases while preserving the original data. [ 1 ]
Uncompressed digital media as well as lossless compression have high memory and bandwidth requirements, which is significantly reduced by the DCT lossy compression technique, [7] [8] capable of achieving data compression ratios from 8:1 to 14:1 for near-studio-quality, [7] up to 100:1 for acceptable-quality content. [8]
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 ...
LZ77 and LZ78 are the two lossless data compression algorithms published in papers by Abraham Lempel and Jacob Ziv in 1977 [1] and 1978. [2] They are also known as Lempel-Ziv 1 (LZ1) and Lempel-Ziv 2 (LZ2) respectively. [3] These two algorithms form the basis for many variations including LZW, LZSS, LZMA and others.
Lossy audio compression typically works with a psychoacoustic model—a model of human hearing perception. Lossy audio formats typically involve the use of a time/frequency domain transform, such as a modified discrete cosine transform. With the psychoacoustic model, masking effects such as frequency masking and temporal masking are exploited ...