Editing JPEG (section)

==Lossless further compression==
From 2004 to 2008, new research emerged on ways to further compress the data contained in JPEG images without modifying the represented image.<ref name="Bauermann">I. Bauermann and E. Steinbacj. Further Lossless Compression of JPEG Images. Proc. of Picture Coding Symposium (PCS 2004), San Francisco, US, December 15–17, 2004.</ref><ref name="Ponomarenko">N. Ponomarenko, K. Egiazarian, V. Lukin and J. Astola. Additional Lossless Compression of JPEG Images, Proc. of the 4th Intl. Symposium on Image and Signal Processing and Analysis (ISPA 2005), Zagreb, Croatia, pp. 117–120, September 15–17, 2005.</ref><ref name="stirner">M. Stirner and G. Seelmann. Improved Redundancy Reduction for JPEG Files. Proc. of Picture Coding Symposium (PCS 2007), Lisbon, Portugal, November 7–9, 2007</ref><ref name="matsuda">Ichiro Matsuda, Yukio Nomoto, Kei Wakabayashi and Susumu Itoh. Lossless Re-encoding of JPEG images using block-adaptive intra prediction. Proceedings of the 16th European Signal Processing Conference (EUSIPCO 2008).</ref> This has applications in scenarios where the original image is only available in JPEG format, and its size needs to be reduced for archiving or transmission. Standard general-purpose compression tools cannot significantly compress JPEG files.

Typically, such schemes take advantage of improvements to the naive scheme for coding DCT coefficients, which fails to take into account:
* Correlations between magnitudes of adjacent coefficients in the same block;
* Correlations between magnitudes of the same coefficient in adjacent blocks;
* Correlations between magnitudes of the same coefficient/block in different channels;
* The DC coefficients when taken together resemble a downscale version of the original image multiplied by a scaling factor. Well-known schemes for lossless coding of continuous-tone images can be applied, achieving somewhat better compression than the [[Huffman code]]d [[DPCM]] used in JPEG.

Some standard but rarely used options already exist in JPEG to improve the efficiency of coding DCT coefficients: the arithmetic coding option, and the progressive coding option (which produces lower bitrates because values for each coefficient are coded independently, and each coefficient has a significantly different distribution). Modern methods have improved on these techniques by reordering coefficients to group coefficients of larger magnitude together;<ref name="Bauermann" /> using adjacent coefficients and blocks to predict new coefficient values;<ref name="stirner" /> dividing blocks or coefficients up among a small number of independently coded models based on their statistics and adjacent values;<ref name="Ponomarenko" /><ref name="stirner" /> and most recently, by decoding blocks, predicting subsequent blocks in the spatial domain, and then encoding these to generate predictions for DCT coefficients.<ref name="matsuda" />

Typically, such methods can compress existing JPEG files between 15 and 25 percent, and for JPEGs compressed at low-quality settings, can produce improvements of up to 65%.<ref name="stirner" /><ref name="matsuda" />

A freely available tool called packJPG is based on the 2007 paper "Improved Redundancy Reduction for JPEG Files." As of version 2.5k of 2016, it reports a typical 20% reduction by transcoding.<ref>{{cite web |last1=Stirner |first1=Matthias |title=packjpg/packJPG |website=[[GitHub]] |url=https://github.com/packjpg/packJPG |date=19 February 2023 |access-date=2 March 2023 |archive-date=2 March 2023 |archive-url=https://web.archive.org/web/20230302013300/https://github.com/packjpg/packJPG |url-status=live }}</ref> [[JPEG XL]] (ISO/IEC 18181) of 2018 reports a similar reduction in its transcoding.