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Lossless codecs use curve fitting or linear prediction as a basis for estimating the signal. Parameters describing the estimation and the difference between the estimation and the actual signal are coded separately. [49] A number of lossless audio compression formats exist. See list of lossless codecs for a listing.
Lossless compression algorithms and their implementations are routinely tested in head-to-head benchmarks. There are a number of better-known compression benchmarks. Some benchmarks cover only the data compression ratio, so winners in these benchmarks may be unsuitable for everyday use due to the slow speed of the top performers. Another ...
JPEG XS favors visually lossless quality in combination with low latency and low complexity, over date reduction through compression. It is not a direct competitor to alternative image codecs like JPEG 2000 and JPEG XL or video codecs like AV1, AVC/H.264 and HEVC/H.265 which tend to focus on compression efficiency. Other important features are:
In data compression and psychoacoustics, transparency is the result of lossy data compression accurate enough that the compressed result is perceptually indistinguishable from the uncompressed input, i.e. perceptually lossless. A transparency threshold is a given value at which transparency is reached. It is commonly used to describe compressed ...
So-called true motion search commonly will not reach minimum sum of absolute differences (SAD) values in codec ME, but may result in better visual quality. Such methods also require more compression time. Rate control strategy. VBR commonly causes better visual quality marks than CBR for the same average PSNR values for sequences.
Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.
Even when noticeable by the user, further data reduction may be desirable (e.g., for real-time communication or to reduce transmission times or storage needs). The most widely used lossy compression algorithm is the discrete cosine transform (DCT), first published by Nasir Ahmed , T. Natarajan and K. R. Rao in 1974.
ISO 29170 more specifically defines an algorithm as visually lossless "when all the observers fail to correctly identify the reference image more than 75% of the trials". [4]: 18 However, the standard allows for images that "exhibit particularly strong artifacts" to be disregarded or excluded from testing, such as engineered test images.