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CS 414 – Multimedia Systems Design Lecture 10 – Compression Basics and JPEG Compression (Part 4)

CS 414 – Multimedia Systems Design Lecture 10 – Compression Basics and JPEG Compression (Part 4). Klara Nahrstedt Spring 2014. Administrative. MP1 is posted. Today Covered Topics. Hybrid Coding: JPEG Coding. JPEG Compression (Baseline). 8x8 blocks. Source Image. Image Preparation. B.

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CS 414 – Multimedia Systems Design Lecture 10 – Compression Basics and JPEG Compression (Part 4)

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  1. CS 414 – Multimedia Systems DesignLecture 10 – Compression Basics and JPEG Compression (Part 4) Klara Nahrstedt Spring 2014 CS 414 - Spring 2014

  2. Administrative • MP1 is posted CS 414 - Spring 2014

  3. Today Covered Topics • Hybrid Coding: • JPEG Coding CS 414 - Spring 2014

  4. JPEG Compression (Baseline) 8x8 blocks SourceImage Image Preparation B G R DCT-based encoding Compressedimage data FDCT Quantizer EntropyEncoder Image Processing Table Table CS 414 - Spring 2014

  5. 1. Image Preparation • Image Preparation: • Source image consists of components (Ci) and to each component we assign YUV or RGB signals. • Image Component Preparation: • Each component divided into blocks (8x8 pixels) • Image Block Preparation: • Blocks (data units) need to be ordered • Interleaved or non-interleaved block ordering

  6. 2. Image Processing • Shift values [0, 2P - 1] to [-2P-1, 2P-1 - 1] • e.g. if (P=8), shift [0, 255] to [-127, 127] • DCT requires range be centered around 0 • Values in 8x8 pixel blocks are spatial values and there are 64 samples values in each block • Transform values • From spatial intensity to frequency values via FDCT transformation (encoder) and • From frequency to spatial intensity values (decoder) CS 414 - Spring 2014

  7. Equations for 2D DCT • Forward DCT: • Inverse DCT:

  8. Visualization of 2D DCT Basis Functions Increasing frequency Increasing frequency CS 414 - Spring 2014

  9. Coefficient Differentiation • F(0,0) • includes the lowest frequency in both directions • is called DC coefficient • Determines fundamental color of the block • F(0,1) …. F(7,7) • are called AC coefficients • Their frequency is non-zero in one or both directions CS 414 - Spring 2014

  10. 3. Quantization • Throw out bits • Consider example: 1011012 = 45 (6 bits) • We can truncate this string to 4 bits: 10112 = 11 • We can truncate this string to 3 bits: 1012 = 5 (original value 40) or 1102 = 6 (original value 48) • Uniform quantization is achieved by dividing DCT coefficients by N and round the result (e.g., above we used N=4 or N=8) • In JPEG – use quantization tables • Fq(u,v) = F(u,v)/Quv • Two quantization tables – one for luminance and one for two chrominance components CS 414 - Spring 2014

  11. De facto Quantization Table Eye becomes less sensitive Eye becomes less sensitive CS 414 - Spring 2014

  12. 4. Entropy Encoding • Compress sequence of quantized DC and AC coefficients from quantization step • further increase compression, without loss • Separate DC from AC components • DC components change slowly, thus will be encoded using difference encoding CS 414 - Spring 2014

  13. DC Encoding • DC represents average intensity of a block • encode using difference encoding scheme • Because difference tends to be near zero, can use less bits in the encoding • categorize difference into difference classes • send the index of the difference class, followed by bits representing the difference CS 414 - Spring 2014

  14. Difference Coding applied to DC Coefficients CS 414 - Spring 2014

  15. AC Encoding • Use zig-zag ordering of coefficients • orders frequency components from low->high • produce maximal series of 0s at the end • Ordering helps to apply efficiently entropy encoding • Apply Huffman coding • Apply RLE on AC zero values CS 414 - Spring 2014

  16. Huffman Encoding • Sequence of DC difference indices and values along with RLE of AC coefficients • Apply Huffman encoding to sequence • Attach appropriate headers • Finally have the JPEG image! CS 414 - Spring 2014

  17. Interchange Format of JPEG CS 414 - Spring 2014

  18. Example - One Everyday Photo 2.76M CS 414 - Spring 2014

  19. Example - One Everyday Photo 600K CS 414 - Spring 2014

  20. Example - One Everyday Photo 350K CS 414 - Spring 2014

  21. Example - One Everyday Photo 240K CS 414 - Spring 2014

  22. Example - One Everyday Photo 144K CS 414 - Spring 2014

  23. Example - One Everyday Photo 88K CS 414 - Spring 2014

  24. Discussion • What types of image content would JPEG work best (worst) for? • Is image compression solved? • What’s missing from JPEG? CS 414 - Spring 2014

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