Jpeg still image data compression standard
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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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JPEG Still Image Data Compression Standard

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Jpeg still image data compression standard

JPEGStill Image Data Compression Standard

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




image data


image data











coding tables

The basic parts of an JPEG encoder

JPEG Overview

Jpeg baseline system

JPEG Baseline System

Jpeg baseline system1






88 blocks

DCT-based encoder


image data



image data





The basic architecture of JPEG Baseline system

JPEG Baseline System

JPEG Baseline system is composed of:

  • Sequential DCT-based mode

  • Huffman coding

Jpeg still image data compression standard

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):


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.

Basis of dct transform

Basis of DCT transform

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



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)

Jpeg still image data compression standard

Additional bits for sign and magnitude

Huffman AC statistical model

run-length/amplitude combinations

Huffman coding of AC coefficients

An examples of statistical modeling

An examples of statistical modeling

Other operation modes jpeg2000 roi coding

Other Operation Modes:JPEG2000 ROI coding

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

Dct transform vs space scale transform

DCT Transform vs. Space-Scale Transform

Jpeg2000 roi coding

JPEG2000 ROI coding

  • Bit plane shift

  • Finer Quantization level used




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