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Transparency, like sound or video quality, is subjective. It depends most on the listener's familiarity with digital artifacts, their awareness that artifacts may in fact be present, and to a lesser extent, the compression method, bit rate used, input characteristics, and the listening/viewing conditions and equipment.
The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm.
Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but it does not generally achieve compression ratio much better than 2:1 because of the intrinsic entropy of the data. Compression algorithms which provide higher ratios either incur very large overheads or work only for specific data ...
Compression algorithms can average a color across these similar areas in a manner similar to those used in JPEG image compression. [10] As in all lossy compression, there is a trade-off between video quality and bit rate, cost of processing the compression and decompression
The Compression Ratings website published a chart summary of the "frontier" in compression ratio and time. [15] The Compression Analysis Tool [16] is a Windows application that enables end users to benchmark the performance characteristics of streaming implementations of LZF4, Deflate, ZLIB, GZIP, BZIP2 and LZMA using their own data. It ...
Requires an additional buffer during compression (of size 8 kB or 64 kB, depending on compression level) Requires no additional memory for decompression other than the source and destination buffers Allows the user to adjust the balance between compression ratio and compression speed, without affecting the speed of decompression
Compression artifacts in compressed audio typically show up as ringing, pre-echo, "birdie artifacts", drop-outs, rattling, warbling, metallic ringing, an underwater feeling, hissing, or "graininess". An example of compression artifacts in audio is applause in a relatively highly compressed audio file (e.g. 96 kbit/sec MP3).
The LZ4 algorithm aims to provide a good trade-off between speed and compression ratio. Typically, it has a smaller (i.e., worse) compression ratio than the similar LZO algorithm, which in turn is worse than algorithms like DEFLATE. However, LZ4 compression speed is similar to LZO and several times faster than DEFLATE, while decompression speed ...