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JPEG Still Image Data Compression Standard. JPEG Introduction - The background. JPEG stands for Joint Photographic Expert Group A standard image compression method is needed to enable interoperability of equipment from different manufacturer

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Presentation Transcript
jpeg introduction the background
JPEG Introduction - The background
  • JPEG stands for Joint Photographic Expert Group
  • A standard image compression method is needed to enable interoperability of equipment from different manufacturer
  • It is the first international digital image compression standard for continuous-tone images (grayscale or color)
  • Why compression is needed?
    • Ex) VGA(640x480)  640x480x8x3=7,372,800bits

with compression  200,000bits without any visual degradation

jpeg introduction what s the objective
JPEG Introduction – what’s the objective?
  • “very good” or “excellent” compression rate, reconstructed image quality, transmission rate
  • be applicable to practically any kind of continuous-tone digital source image
  • good complexity
  • have the following modes of operations:
    • sequential encoding
    • Progressive encoding
    • lossless encoding
jpeg overview
entropy

encoder

compressed

image data

Source

image data

descriptors

symbols

encoder

statistical

model

Encoder

model

model

tables

entropy

coding tables

The basic parts of an JPEG encoder

JPEG Overview
jpeg baseline system1
statistical

model

entropy

encoder

quantizer

88 blocks

DCT-based encoder

compressed

image data

FDCT

Source

image data

table

specification

table

specification

The basic architecture of JPEG Baseline system

JPEG Baseline System

JPEG Baseline system is composed of:

  • Sequential DCT-based mode
  • Huffman coding
slide7
JPEG Baseline System

– Why does it work?

Frequency sensitivity of Human Visual System

  • Lossy encoding
  • HVS is generally more sensitive to low frequencies
  • Natural images
the baseline system dct
The mathematical representation of FDCT (2-D):

Where

f(x,y): 2-D sample value

F(u,v): 2-D DCT coefficient

The Baseline System – DCT
  • The Discrete Cosine Transform (DCT) separates the frequencies contained in an image.
  • The original data could be reconstructed by Inverse DCT.
the baseline system dct cont
An example of 1-D DCT decomposition

Before DCT (image data)

After DCT (coefficients)

The 8 basic functions for 1-D DCT

The Baseline System-DCT (cont.)
the baseline system dct cont1
The Baseline System-DCT (cont.)
  • The DCT coefficient values can be regarded as the relative amounts of the 2-D spatial frequencies contained in the 88 block
  • the upper-left corner coefficient is called the DC coefficient, which is a measure of the average of the energy of the block
  • Other coefficients are called AC coefficients, coefficients correspond to high frequencies tend to be zero or near zero for most natural images
the baseline system quantization
The Baseline System – Quantization
  • Why quantization? .
    • to achieve further compression by representing DCT coefficients with no greater precision than is necessary to achieve the desired image quality
  • Generally, the “high frequency coefficients” has larger quantization values
  • Quantization makes most coefficients to be zero, it makes the compression system efficient, but it’s the main source that make the system “lossy”

F(u,v): original DCT coefficient

F’(u,v): DCT coefficient after quantization

Q(u,v): quantization value

the baseline system quantization cont
The Baseline System-Quantization (cont.)

JPEG Luminance quantization table

a simple example
Original image pattern

After FDCT(DCT coefficients)

Digitized image

A simple example
a simple example cont
A simple example(cont.)

DCT coefficients

Quantized coefficients

baseline system dc coefficient coding
DC difference

quantized DC

coefficients

DPCM

Differential pulse code modulation

Baseline System - DC coefficient coding
  • Since most image samples have correlation and DC coefficient is a measure of the average value of a 88 block, we make use of the “correlation” of DC coefficients
baseline system ac coefficient coding
Horizontal frequency

Vertical frequency

Baseline System - AC coefficient coding
  • AC coefficients are arranged into a zig-zag sequence:
  • 3 0 0 -3 0 -3 0 0 0 0

-1 0 -2(EOB)

baseline system statistical modeling
Baseline System - Statistical modeling
  • Statistical modeling translate the inputs to a sequence of “symbols” for Huffman coding to use
  • Statistical modeling on DC coefficients:
    • symbol 1: different size (SSSS)
    • symbol 2: amplitude of difference (additional bits)
  • Statistical modeling on AC coefficients:
    • symbol 1: RUN-SIZE=16*RRRR+SSSS
    • symbol 2: amplitude of difference (additional bits)
slide19
Additional bits for sign and magnitude

Huffman AC statistical model

run-length/amplitude combinations

Huffman coding of AC coefficients

jpeg 2000
JPEG 2000
  • Allow efficient lossy and lossless compression within a single unified coding framework
  • Progressive transmission by quality, resolution, component, or spatial locality
  • Compressed domain processing
  • Region of Interest coding
  • JPEG2000 is NOT an extension of JPEG
    • Wavelet Transform
    • An extremely flexible bitstream structure
jpeg2000 roi coding
JPEG2000 ROI coding
  • Bit plane shift
  • Finer Quantization level used
experiment
Experiment
  • http://www.sfu.ca/~cjenning/toybox/hjpeg/index.html
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