compression2.slides.printing

Perceptual redundancy perceptual redundancy eye is

This preview shows page 14 - 21 out of 23 pages.

Perceptual Redundancy Perceptual Redundancy Eye is less sensitive to Color High Frequencies So, Allocate more bits/samples to intensity than chromaticity. Allocate more bits to low frequencies than to high frequencies Can play similar tricks with the ear and varying sensitivity to different frequencies (e.g., the “psychoacoustic model” plays a key role in MP3).
Image of page 14

Subscribe to view the full document.

Compression (cont’d) Perceptual Redundancy Block Transform Coding Block Transform Coding Use a transform to convert from spatial domain to another (e.g., a frequency-based one) Win #1: many transforms “pack” the information into parts of the domain better than spatial representations Win #2: Quantize coefficients according to perception (e.g., quantize high frequencies more coarsely than low ones) Problem: artifacts caused by imperfect approximation in one place get spread across the entire image Solution: independently transform and quantize blocks of the image: block transform encoding
Image of page 15
Compression (cont’d) Perceptual Redundancy Block Transform Coding Transform Coding: General Structure
Image of page 16

Subscribe to view the full document.

Compression (cont’d) Perceptual Redundancy Block Transform Coding Transform Coding There are many other transforms besides the Fourier Transform, all with the same structure: Forward transform (general form): T ( u , v ) = M X x = 0 N X y = 0 f ( x , y ) g ( x , y , u , v ) Inverse transform (general form): f ( x , y ) = M X u = 0 N X v = 0 T ( u , v ) h ( x , y , u , v ) Most of the time, g ( x , y , u , v ) = h ( x , y , u , v ) (possible normalized, or complex conjugates)
Image of page 17
Compression (cont’d) Perceptual Redundancy Block Transform Coding Transform Coding Other basis sets: Walsh-Hadamard Cosine
Image of page 18

Subscribe to view the full document.

Compression (cont’d) Perceptual Redundancy Block Transform Coding Discrete Cosine Transform g ( x , y , u , v ) = h ( x , y , u , v ) = α ( u ) α ( v ) cos ( 2 x + 1 ) u π 2 N cos ( 2 y + 1 ) v π 2 N where α ( u ) = 1 N if u = 0 2 N otherwise
Image of page 19
Compression (cont’d) Perceptual Redundancy Block Transform Coding Discrete Cosine Transform How can we get away with cosines and no sines?
Image of page 20

Subscribe to view the full document.

Image of page 21
You've reached the end of this preview.
  • Winter '08
  • Morse,B
  • (Cont’d), Codebook, interpixel redundancy, entropy coding

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern