1 / 15

JPEG-LS -- The new standard of lossless image compression

JPEG-LS -- The new standard of lossless image compression. School of Computer Science, University of Central Florida, VLSI and M-5 Research Group. Predictive encoding. Q: why prediction? A: to produce a more “skewed” set of sequence for entropy encoder. Prediction example.

dory
Download Presentation

JPEG-LS -- The new standard of lossless image compression

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. JPEG-LS-- The new standard of lossless image compression School of Computer Science, University of Central Florida, VLSI and M-5 Research Group

  2. Predictive encoding • Q: why prediction? • A: to produce a more “skewed” set of sequence for entropy encoder Prediction example

  3. Predictive encoding • Lossless JPEG • JPEG-LS • CALIC • ……

  4. C B A X What’s wrong with Lossless JPEG ? • Lossless JPEG uses “static” predictor. The prediction model is determined before the compression starts

  5. JPEG-LS is better because… • “Dynamic” predictor. The predictor is determined dynamically • Simple edge detection algorithm is introduced to determine the predictor. • Prediction refinement

  6. c b d a X JPEG-LS: initial prediction Prediction algorithm: • Xis the pixel being encoded • a,b and c used for initial prediction If c  max(a, b) X^ = min(a, b) Else { If c  min(a, b) X^ = max(a, b) Else X^= a + b- c }

  7. 103 99 100 100 d d 102 102 X X JPEG-LS: initial prediction example • X is predicted as 100 since a vertical edge is detected • X is predicted as 102 since a horizontal edge is detected

  8. JPEG-LS: refine the prediction • JPEG-LS maintains 365 contexts. it is used to describe the local “characteristics” of pixels. • Context of X is computed from a, b and d • Each context maintains a bias, which can be considered as an evaluation of the predictor’s performance for that particular context. • The bias is used for refinement of the initial prediction • X’’ = X’ – B[q] • where X’ is the initial prediction, X’’ is the refined prediction, q is the context of X and B is the bias

  9. 103 100 d 102 X JPEG-LS: prediction refinement example • X’ = 100 • Suppose X is in context q and B[q] = -1, then X’’ = 100 – (-1) = 101

  10. JPEG-LS: compute the prediction error, update the bias • Compute the prediction error as: • Prediction Error = X – X’’ • Update the context-dependent bias

  11. Ready for entropy encoding? Wait……

  12. JPEG-LS: Re-mapping example • If the pixel value is in range [0, 255], the prediction error is in range [-255, 255] • A larger range means more bits to represent the prediction error • We need to re-map the prediction errors to [0, 255], is it possible? • Yes. Since the prediction error always in range [-x”, 255-x’’]

  13. JPEG-LS: Re-map residuals The following example assumes pixel value is in [0, 7] Mistake in David Salomon’s book? X’’’ = 2x’’ if x’’>=0 X’’’ = 2|x’’|-1 otherwise

  14. JPEG-LS: entropy encoding • Finally entropy encoder is applied.

  15. Summary of the basic steps of JPEG-LS • Find the initial prediction X^ • Refine prediction by considering the bias of the context • Compute the prediction error (residual) and update the bias of that context • Re-map residuals • Encode residuals using Golomb-Rice coder

More Related