Colour Image Compression by Colour Image Compression by Grey to Colour ConversionGrey to Colour Conversion

Mark S. Drew1, Graham D. Finlayson2, and Abhilash Jindal3

1Simon Fraser University 2University of East Anglia

3IIT Kanpur,1mark@cs.sfu.ca

2graham@cmp.uea.ac.uk 3ajindal@iitk.ac.in

1. The problem:

• Can we use a map from Grey, onto Colour, so as to transmit/store just Grey + some parameters, and re-generate full Colour? • Here we utilize a polynomial map, from achromatic information to each of R,G,B

2. Color Spaces

• Log-Geometric-Mean Luminance• Mean Luminance• HSV Color Space• YIQ Color Space• YCbCr Color Space• AC1C2 Color Space

HSV-Hue HSV-Sat HSV-Value

3. Windowing Method

• Divide image into fixed-size windows• Plot chrominance (2-D) vs. luminance for each window• Polynomial regression of luminance onto chrominance • Quantize coefficients obtained from regression• Compress luminance information (e.g. JPEG)

4. Observation:• Good performance for smooth color variations• Error in case of an edge

compre

ssed

1: no edge

2: contains edge

original

chrom_1 chrom_2

{

Box_1 (smooth) Good regression!

2: contains edge chrom_1 chrom_2

{Box_2 (edge) worse regression!

5. Observation: cont.

6. Pyramid Approach

• Polynomial regression of chrominance vs. luminance• If PSNR < threshold• Break image into four parts• Repeat regression for each part• If PSNR > threshold• Go to next part

7. Segmentation Method:

• Segment input image• Choose the pixels in one segment• Polynomial regression of chrominance vs. luminance

8. Separate Sorting:• Sort Chrominances of the image separately• Make chunks of fixed number of pixels• Polynomial regression of chrominance vs. luminance

Sorted chrominance components (in log-geometric-mean colour space). Here, the original RGB image is shown with addressing according to sorting respectively the first and second chrominance channels.

9. Evaluation of Results:

• Peak Signal to Noise Ratio PSNR

• Spatial-CIELAB (S-CIELAB) Error ΔES

== a spatial blurring is applied to the colour image data to simulate the human visual system. Also, the blurring function is adjusted differently for three different colour planes according to human psychophysical measurements of colourappearance.

•Compression Rate the Shannon entropy== quantize polynomial regression coefficients linearly over their maximum and minimum limit. Then use LB = Shannon entropy.

10. Comparision of Colour Spaces:

PSNR vs. bits per pixelS-CIELAB Error ΔE vs. bits per pixel.

Using simple windowing method:

log-geometric-mean colour space isbest colour space

11. Comparision of algorithms:

performance for the idea of separately sorting each chrominance component is much better than all the other methods

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12. Main conclusions:

→ Developed a much faster and quite efficient algorithm for image compression→ Instead of de-correlating image luminance from chrominance, use the correlation between the luminance component of an image and its chromatic components

→ log-geometric-mean color space is best color space

→ separate sorting is best algorithm

Future Work

→ Extend to video