# JPEG Still Image Data Compression Standard JPEG Introduction

- Slides: 25

JPEG Still Image Data Compression Standard

JPEG Introduction - The background n n 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? n Ex) VGA(640 x 480) 640 x 480 x 8 x 3=7, 372, 800 bits with compression 200, 000 bits without any visual degradation

JPEG Introduction – what’s the objective? n n “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: n n n sequential encoding Progressive encoding lossless encoding

JPEG Overview Source image data descriptors Encoder model tables encoder statistical model symbols entropy encoder entropy coding tables The basic parts of an JPEG encoder compressed image data

JPEG Baseline System

JPEG Baseline System JPEG Baseline system is composed of: n n Sequential DCT-based mode Huffman coding 8 8 blocks DCT-based encoder FDCT Source image data quantizer statistical model entropy encoder table specification The basic architecture of JPEG Baseline system compressed image data table specification

JPEG Baseline System – Why does it work? n n n Lossy encoding HVS is generally more sensitive to low frequencies Natural images Frequency sensitivity of Human Visual System

The Baseline System – DCT n n The Discrete Cosine Transform (DCT) separates the frequencies contained in an image. The original data could be reconstructed by Inverse DCT. The mathematical representation of FDCT (2 -D): Where f(x, y): 2 -D sample value F(u, v): 2 -D DCT coefficient

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. ) n n n 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 F(u, v): original DCT coefficient F’(u, v): DCT coefficient after quantization Q(u, v): quantization value n Why quantization? . n n n 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”

The Baseline System-Quantization (cont. ) JPEG Luminance quantization table

A simple example Original image pattern Digitized image After FDCT(DCT coefficients)

A simple example(cont. ) DCT coefficients Quantized coefficients

Baseline System - DC coefficient coding n 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 quantized DC coefficients DPCM DC difference Differential pulse code modulation

Baseline System - AC coefficient coding n AC coefficients are arranged into a zig-zag sequence: Vertical frequency Horizontal frequency à 3 0 0 -3 0 0 -1 0 -2(EOB)

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

Additional bits for sign and magnitude Huffman AC statistical model run-length/amplitude combinations Huffman coding of AC coefficients

An examples of statistical modeling

Other Operation Modes: JPEG 2000 ROI coding

JPEG 2000 n n 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 JPEG 2000 is NOT an extension of JPEG n Wavelet Transform n An extremely flexible bitstream structure

DCT Transform vs. Space-Scale Transform

JPEG 2000 ROI coding n n Bit plane shift Finer Quantization level used

Experiment n http: //www. sfu. ca/~cjenning/toybox/hj peg/index. html

- Jpeg still image data compression standard
- Jpeg still image data compression standard
- Block size in block preparation step of jpeg compression is
- What's wrong in the picture
- Arithmetic coding in digital image processing
- Subjective fidelity criteria in digital image processing
- Jpeg in digital image processing
- What is
- Data compression in data mining
- Quantization in data compression
- Coding redundancy works on
- Lossless compression in digital image processing
- Fractal image compression example
- Singular value decomposition image compression
- Signal image compression
- Still image in drama
- What is lossy data compression
- Adaptive huffman
- Uniquely decodable code
- Merrval
- Data compression test
- Examples of lossy and lossless compression
- Mpeg advantages and disadvantages
- Jpeg camera module
- Mpeg vs jpeg
- Jpeg analyzer