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IMPROVING THE PERFORMANCE OF JPEG-LS Michael Syme 18171613 Supervisor: Dr. Peter Tischer.

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Presentation on theme: "IMPROVING THE PERFORMANCE OF JPEG-LS Michael Syme 18171613 Supervisor: Dr. Peter Tischer."— Presentation transcript:

1 IMPROVING THE PERFORMANCE OF JPEG-LS Michael Syme 18171613 Supervisor: Dr. Peter Tischer

2 2 Program Background JPEG-LS Schematics Where can JPEG-LS be improved Solutions

3 3 Introduction Terms: Lossy Compression: Loss of data; Loss in image quality. Lossless Compression: No loss of data; No loss in image quality. Near Lossless Compression: Loss of data up to a predetermined amount (Pd) over all pixel values. Eg: No pixel value can change by more than ‘t’.

4 4 Introduction Cont’d LOCO-I (Low Complexity Lossless Compression of Images) is the algorithm at the heart of JPEG-LS. JPEG-LS was introduced as a standard in 1999 for lossless compression. JPEG-LS is not optimal. It achieves good compression and high throughput for a low complexity algorithm. Therefore, it can be improved!

5 5 Introduction Cont’d There has been extensive research on the improvement of lossless compression mode of JPEG-LS, but very little on the near lossless compression mode. Therefore, one major aim of this thesis is to improve the near lossless compression mode of JPEG-LS.

6 6 JPEG-LS Description Terms: Predicted Value: JPEG-LS predicts the current pixel value by making an estimation from its neighbouring pixels. Prediction Error: After some internal adjustments of the predicted value, the prediction error is calculated and encoded. It is important that the prediction error is small.

7 7 JPEG-LS Description Cont’d Terms: Causal Neighbourhood: Neighbourhood of pixels surrounding the current pixel. Common Knowledge: Previous pixel values which both the encoder and decoder know.

8 8 Coding Terms: Flat Region: When the pixels surrounding the current pixel, are equal or less than a predetermined amount. Gradients: d1 = d – a; d2 = a – c; d3 = c – b; Flat region: d1,d2,d3 <= Pd

9 9 Where can JPEG-LS be improved? Coding: Achieve better compression than 1 bit per pixel Predictor: Not much research has been done on prediction for near lossless coding. Applying JPEG-LS to Coding for Regions of Interest. i.e. Allowing different amounts of loss in different parts of the image.

10 10 Proposed Coding Solution Solution: Attach a Run Length Coding technique as a post process operation to JPEG-LS which will take the encoded JPEG-LS file, and output a more compressed file. There are two possible improvement techniques.

11 11 Proposed Coding Solutions Cont’d A Run Length Coder detects and specially encodes repetitive bits and long streams of 0’s and 1’s. A Whitening Transform procedure detects repeating bit stream patterns of 1’s and 0’s. It converts the compressed file into long streams of 0’s and 1’s, which a RLC can then encode.

12 12 Predictor Solutions Predicted value will have a noise component and a signal component. We may want to minimize the noise component.

13 13 Region of Interest Coding Lena can be separated from the background. Her eyes can be separated from her face. How do we know what to encode in lossless and near lossless?

14 14 Region of Interest Coding Solution Coding for regions of interest can be performed in two ways: 1. Includes the encoding of extra information to make explicit, how much loss is to be tolerated. 2. Encoder and Decoder decide to vary the degree of loss based on the common knowledge. Therefore, there is no need for side information.

15 15 Conclusion Hope to improve the performance of JPEG-LS by: 1. Implementing post process methods to reduce the compression size of JPEG-LS files. 2. Reducing the influence of noise and to utilise the functionality of the near lossless compression mode. 3. Using the near lossless and lossless compression modes of JPEG-LS for coding regions of interest.

16 16 Conclusion Cont’d A public domain/ open source JPEG-LS coder can be found at: Further information about JPEG-LS can be found at: A viewer of JPEG-LS files can be downloaded from Pegasus at:

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