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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.
optimisation of the Huffman coding layer of a JPEG file to increase compression, conversion between progressive and sequential JPEG formats, conversion between Huffman and arithmetic coding in the entropy coding layer. [5] [6] [7] These transformations are each completely lossless and reversible. The transformations on the image data comprise:
Besides uncompressed formats and lossless compression formats that can usually be interconverted without any loss of detail, there are compressed formats such as JPEG, which lose detail on nearly every compress. While a conversion from a compressed to an uncompressed format is in general without loss, this is not true the other way around.
JPEG XS is a light-weight compression that visually preserves the quality compared to an uncompressed stream, at a low cost, targeted at compression ratios of up to 10:1. With XS, it is for example possible to repurpose existing SDI cables to transport 4K60 over a single 3G-SDI (at 4:1), and even over a single HD-SDI (at 8:1).
[5] [6] This technology is integrated into JPEGmini Pro allowing users to reduce the file size of their photos and videos with limited quality reduction. JPEGmini technology is built around a perceptually aligned image quality measure, which reliably determines the maximum amount of compression which can be applied to each individual image or ...
Continuously varied JPEG compression (between Q=100 and Q=1) for an abdominal CT scan. JPEG (/ ˈ dʒ eɪ p ɛ ɡ / JAY-peg, short for Joint Photographic Experts Group and sometimes retroactively referred to as JPEG 1) [2] [3] is a commonly used method of lossy compression for digital images, particularly for those images produced by digital photography.
Guetzli supports only the top of JPEG's quality range (quantizer settings 84–100) [7] [8] and supports only sequential (non-"progressive") encoding. Guetzli is more effective with bigger files. [8] Google says it is a demonstration of the potential of psychovisual optimizations, intended to motivate further research into future JPEG encoders. [2]
JPEG transcoding: Being a JPEG superset, JXL provides efficient lossless recompression options for images in the traditional/legacy JPEG format that can represent JPEG data in a more space-efficient way (~20% size reduction due to the better entropy coder) and can easily be reversed, e.g. on the fly. Wrapped inside a JPEG XL file/stream, it can ...
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