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

2. CS 414 - Spring 2014 Administrative MP1 is posted

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

4. JPEG Compression (Baseline) FDCT Source Image Quantizer Entropy Encoder Table Compressed image data DCT-based encoding 8x8 blocks R B G CS 414 - Spring 2014 Image Preparation Image Processing

5. 1. Image Preparation Image Preparation:  Source image consists of components (C i ) 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, 2 P - 1] to [-2 P-1, 2 P-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 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: 101101 2 = 45 (6 bits)  We can truncate this string to 4 bits: 1011 2 = 11  We can truncate this string to 3 bits: 101 2 = 5 (original value 40) or 110 2 = 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)/Q uv  Two quantization tables – one for luminance and one for two chrominance components CS 414 - Spring 2014

11. De facto Quantization Table 1611101624405161 12 141926586055 1413162440576956 1417222951878062 182237566810910377 243555648110411392 49647887103121120101 7292959811210010399 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