Chapter 5 : IMAGE COMPRESSION – LOSSLESS COMPRESSION - 1)Asmawati @ Nur Hidayah Bte Jusoh (IT...
-
Upload
rosemary-taylor -
Category
Documents
-
view
227 -
download
7
Transcript of Chapter 5 : IMAGE COMPRESSION – LOSSLESS COMPRESSION - 1)Asmawati @ Nur Hidayah Bte Jusoh (IT...
Chapter 5 : IMAGE COMPRESSION – LOSSLESS
COMPRESSION -
1) Asmawati @ Nur Hidayah Bte Jusoh (IT 01481)
2) Azmah Bte Abdullah Sani (IT 01494)
3) Dina Meliwana Bte Asjhad Zaenie (IT 02373)
4) Ummi Susanti Bte Rafiei (IT 02145)
Contents:
5.1. Overview of Image Compression
5.2. Lossless Compression Methods
5.3. Application Of Lossless Compression
5.4. Recent Research Of Lossless Compression.
Overview Of Image Compression
- Process of reducing or compressing size and image data
files but still retaining important information.
- compressed file is used to reconstruct image.
- relationship between compressed and uncompressed file is
denoted as the compression ratio :
Introduction
Compression Ratio =Uncompressed file size
Compressed file size=
SIZEU
SIZEC
Fidelity Criteria
Fidelity Criteria
Criterion used to determine important information to be
retained when compressing files.
Divided into 2 classes :
i. Objective fidelity criteria ii.Subjective fidelity criteria
What are the objective fidelity criteria & subjective fidelity
criteria??
Fidelity Criteria
Errors are measured using :
1. ROOT- MEAN-SQUARE ERROR
2. ROOT-MEAN-SQUARE SIGNAL-TO-NOISE RATIO(SNR)
3. PEAK SIGNAL-TO-NOISE RATIO
Objective fidelity criteria measures amount of error in
decompressed image. The smaller the value of error metrics,
the better the compressed image.
Subjective testing is performed by creating a database of
image to be tested.
Fidelity Criteria
Images will be tested by having all test subjective to evaluate
them according to a predefined scoring criterion.
The results are then analyzed statistically using average &
standard deviations as metrics.
3 category of tests:
1. IMPAIRMENT TEST
assess images in terms of how bad they are
2. QUALITY TEST
assess images in terms of how good they are
3. COMPARISON TEST
evaluate images on a side-by side basis
Compression System Model
1) Compressor Preprocessing
- Data reduction : Reduce image by using gray level and/or spatial quantization or any image enhancement process
- Mapping : Map image data into other mathematical space ( easier to compress data )
Encoding
- Quantization : Takes potentially continuous from mapping stage and put in discrete form
- Coding : Maps discrete data onto a code
Compression System Model
2) Decompressor Decoding
- Reverse coding of compressed file by mapping to original
Inverse Mapping
- Reverse mapping process Postprocessing
- Enhance the look of final image
Lossless Compression Method
Lossless Compression Methods:
1) Huffman Coding
2) Run – Length Coding
3) Lempel – Ziv – Welch Coding
4) Arithmetic Coding
Introduction :
Lossless compression methods guarantees that the decompressed image is absolutely identical to the image before compression.
Lossless Compression Methods<< HUFFMAN CODING >>
HUFFMAN CODING
- developed by D. A. Huffman
- based on the fact that in an input stream certain tokens
occur more often than others.
- generating codes that closest to entrophy.
Lossless Compresion Methods<<Step By Step >>
Step By Step…
Find the gray – level probabilities for the image by
finding the histogram. Order the input probabilities (histogram magnitudes )
from smallest to largest. Combine the smallest two. (add the two smallest) GOTO step 2, until only two probabilities are left. By working backward along the tree, generate code by
alternating assignment of 0 and 1.
Lossless Compression Methods<< RUN – LENGTH CODING >>
RUN – LENGTH CODING
- used when there is a tendency for long runs of repeated
digitized gray levels to occur.
- the Run-length coding (RLC) works by counting the number
of adjacent pixels with the same gray-level value.
Lossless Compression Methods<< Step By Step >>
Step By Step…
Define the required parameters horizontally or vertically. Define a convention for the first RLC number in a row
(represent a run of 0’s or 1’s). Extend basic RLC using Bit-plane RLC – works by
applying basic RLC to each bit plane independently. The compression results is then improved using preprocessing
to reduce the number of gray level. Then , the reduce image data mapped to a Gray Code,
Lossless Compression Methods<< Example >>
0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0
1 1 0 0 1 1 0 0
0 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1
0 1 0 1 0 1 0 1
0 0 0 0 1 1 1 0
0 1 0 1 1 0 0 0
Horizontal Run Length Coding Using the convention of 0’s as the first value.
No of rowsROW 1
ROW 2
ROW 4
ROW 5
ROW 3
ROW 6
ROW 8ROW 7
8
4, 1, 3
1, 2, 2, 2, 21, 6, 1
1, 81, 1, 1, 1, 1, 1, 1, 1
4, 3, 11, 1, 1, 2, 3
Lossless Compresion Methods<<Lempel-Ziv-Welch Coding>>
LEMPEL-ZIV-WELCH CODING (LZW)
- works by coding strings of data
- for images, the strings correspond to sequence of pixel value
- a string table that contains the strings and their corresponding value is created
- the table is updated as the file is read, with new codes being inserted whenever a new string is encountered
Lossless Compresion Methods<<Lempel-Ziv-Welch Coding>>
- if the table already contains the string, the corresponding code for that string is put into the compressed file
- uses code words with more bits than the original data
- the table consists of the original 2n entries (corresponding to the original n-bit data) and allows another 2n-k - 2n entries for string codes
Lossless Compression Method<<Arithmetic Coding>>
ARITHMETIC CODING
- transforms input data into a single floating point between
0 and 1
- the image must be divided into small subimages to be encoded
- works by successively subdividing the interval between 0 and 1
- impractical to use alone
Lossless Compresion Methods<<Arithmetic Coding>>
Step By Step…
Find the probability for each pixel
Divide the initial subinterval based on the distribution
The first pixel value is coded by extracting the subinterval corresponding to it, and subdivide again based on the same relative distribution
Repeat step (3) for each pixel value until a final interval is determined
Any value within this subinterval can be used to represent the sequence of gray-level values
Application Of Lossless Compression
QuickTime ‘Animation’ Codec
- suited for storage of two-dimensional animation and computer-
generated video content.
PICTools Medical Compression Toolkit’s High Speed
Lossless JPEG by Pegasus.
- uses in the field of cardiology and ultrasound
imaging.
Application Of Lossless Compression
Voice E-mail 4.0 (Voice E-mail’s Lossless Audio
Compression)
- uses the latest in digital audio compression technology to
compress messages before transferring them through
CompuServe, America Online, Microsoft Network, and the
Internet.
Recent Study @ Research Of Lossless Compression…
PPG Lossless Image Compression Project by UCLA
Develop/implement a lossless compression/decompression scheme for use
with the UCLA PACS Image Archive satisfying the following
Key Technical Requirements:
1. Relatively high speeds of compression/decompression. 2. Flexible enough to work with image data from all modalities. 3. Robust/high-reliability code and support environment (e.g. compilers). 4. Longevity, i.e. be able to restore original images after 20 years.