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Lecture 14 Announcements Image Compression and Watermarking Image Processing CSE 166 Lecture 14 Announcements • Assignment 4 is due May 20, 11:59 PM • Assignment 5 will be released May 20 – Due May 29, 11:59 PM • Reading – Chapter 8: Image Compression and Watermarking • Sections 8.1, 8.9, 8.10, and 8.12 CSE 166, Spring 2020 2 Data compression • Data redundancy where compression ratio where CSE 166, Spring 2020 3 Data redundancy in images Coding Spatial Irrelevant redundancy redundancy information Does not need all Information is Information is 8 bits unnecessarily not useful replicated CSE 166, Spring 2020 4 Image information • Entropy where – It is not possible to encode input image with fewer than bits/pixel CSE 166, Spring 2020 5 Fidelity criteria, objective (quantitative) • Total error • Root-mean-square error • Mean-square signal to noise ratio (SNR) CSE 166, Spring 2020 6 Fidelity criteria, subjective (qualitative) CSE 166, Spring 2020 7 Approximations Objective (quantitative) quality rms error (in intensity levels) Lower is 5.17 15.67 14.17 better (a) (b) (c) (a) is better Subjective (qualitative) quality, relative than (b), (b) is better CSE 166, Spring 2020 than (c)8 Compression system CSE 166, Spring 2020 9 Compression methods • Huffman coding • Golomb coding • Arithmetic coding • Lempel-Ziv-Welch (LZW) coding • Run-length coding • Symbol-based coding • Bit-plane coding • Block transform coding • Predictive coding • Wavelet coding CSE 166, Spring 2020 10 Symbol-based coding (0,2) (3,10) … CSE 166, Spring 2020 11 Block-transform coding Encoder Decoder CSE 166, Spring 2020 12 Block-transform coding • Example: discrete cosine transform where • Inverse CSE 166, Spring 2020 13 Block-transform coding 4x4 subimages (4x4 basis images) Walsh-Hadamard transform Discrete cosine transform CSE 166, Spring 2020 14 Block-transform coding 8x8 Walsh- subimages Fourier Hadamard cosine transform transform transform Retain 32 largest coefficients Error image Lower is rms error 2.32 1.78 1.13 CSE 166, Spring 2020 better 15 Block-transform coding Reconstruction error versus subimage size DCT subimage size: 2x2 4x4 8x8 CSE 166, Spring 2020 16 JPEG uses block DCT-based coding Zoomed Compression Scaled error compression reconstruction image reconstruction 25:1 Compression ratio 52:1 CSE 166, Spring 2020 17 Predictive coding model Encoder Decoder CSE 166, Spring 2020 18 Predictive coding Example: previous pixel coding Input image Histograms Prediction error image CSE 166, Spring 2020 19 Wavelet coding Encoder Decoder CSE 166, Spring 2020 20 Wavelet coding Detail coefficients below 25 are truncated to zero CSE 166, Spring 2020 21 JPEG-2000 uses wavelet-based coding Zoomed Compression Scaled error compression reconstruction image reconstruction 25:1 Compression ratio 52:1 CSE 166, Spring 2020 22 JPEG-2000 uses wavelet-based coding Zoomed Compression Scaled error compression reconstruction image reconstruction 75:1 Compression ratio 105:1 CSE 166, Spring 2020 23 Image watermarking • Visible watermarks • Invisible watermarks CSE 166, Spring 2020 24 Visible watermark Watermark Watermarked Original image image minus watermark CSE 166, Spring 2020 25 Invisible image watermarking system Encoder Decoder CSE 166, Spring 2020 26 Invisible watermark Example: watermarking using two least significant bits Original image Extracted watermark Two least significant bits JPEG Fragile compressed invisible watermark CSE 166, Spring 2020 27 Invisible watermark Example: DCT-based watermarking Watermarked Extracted images robust (different invisible watermarks) watermark CSE 166, Spring 2020 28 Next Lecture • Morphological image processing • Reading – Chapter 9: Morphological image processing • Sections 9.1, 9.2, 9.3, and 9.5 (through subsection connected components) CSE 166, Spring 2020 29.
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