amanda
Uploaded by
9 SLIDES
261 VIEWS
100LIKES

Efficient Image Compression Using DPCM with Predictive Statistical Modeling

DESCRIPTION

This document explores a predictive approach to image compression using Differential Pulse Code Modulation (DPCM) and nonuniform quantization techniques. By leveraging the statistical properties of pixel differences based on past and future samples, we achieve significant compression ratios, approximately 2.5:1. The method includes analyzing horizontal and vertical differences statistically to enhance predictive accuracy. Practical coding examples illustrate the extraction of prediction error using standard image datasets. Results demonstrate the efficacy of DPCM in reducing image data while maintaining quality.

1 / 9

Download Presentation

Efficient Image Compression Using DPCM with Predictive Statistical Modeling

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. Prediction-based coding

  2. Input Image Compressed Coded Image Nonuniform quantizer Statistical coder PREDICTOR DPCM coding Dr.E. Regentova

  3. “Past” samples - 0.2 - 0.3 - 0.2 Current pixel - 0.3 “Future” samples 2-D prediction for row-column scanning method Dr.E. Regentova

  4. Compressed Image Decompressed Image Statistical decoder Predictor DPCM Decompression Dr.E. Regentova

  5. Coding differential image Compression ratio is about 2.5(1-3) Dr.E. Regentova

  6. Input image; std=52.85 Input image histogram Histogram of horizontal differences 6000 500 5000 400 4000 300 3000 200 2000 100 1000 0 0 50 100 150 200 250 50 100 150 200 250 Horizontal differences;std=22.4565 Dr.E. Regentova

  7. Horizontal differences; std=22.4565 Histogram of horizontal differences Vertical differences; std=17.0269 Histogram of vertical differences 7000 6000 5000 4000 3000 2000 1000 0 250 50 100 150 200 6000 5000 4000 3000 2000 1000 0 200 250 50 100 150 Dr.E. Regentova

  8. Coding Example DPCM_1D; rate=3 b/pixel , Std of the restoration error 6.67 Lena Dr.E. Regentova

  9. Find the horisontal prediction error J=double(imread('cameraman.tif')); for x= 2:1:256 for y=1:1:256 o=x-1; ImDiff(x,y)= uint16((abs(J(x,y)-J(o,y)))); end end image(ImDiff(:,:); K= double(ImDiff); hist(K); Dr.E. Regentova

More Related