Page 1 CS Department Parallel Design of JPEG2000 Image Compression Xiuzhen Huang CS Department UC Santa Barbara April 30th, 2003

Page 1CS Department

Parallel Design of JPEG2000 Image Compression

Xiuzhen Huang

CS DepartmentUC Santa Barbara

April 30th, 2003

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Outline

• Introduction to image compression• JPEG2000 compression scheme• Parallel implementation of JPEG2000

– On distributed-memory multiprocessors– On shared-memory multiprocessors

• Conclusion

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Introduction to Image Compression

Why do we need image compression?

1280 pixels

800 pixels

1280 800 3 (RGB)= 3 M bytes

File size of a small digital photo without compression:

To speedup the image transmission over Internetand reduce image storage space, we need compression

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Introduction to Image Compression

Original Picture3 M bytes

JPEG2000 Compression19 K bytes

• Compression Ratio: >150 times !• No noticeable difference in picture quality

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JPEG2000 International Standard

JPEG2000: the new international standard for image compression, is much more efficient than the old JPEG international standard.For the same compression ratio / bit rate / file size, the JPEG2000 picturehas much better quality.

JPEG JPEG2000Original Picture

Compression ratio : 50:1

Strong blockiness

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JPEG2000 International Standard

JPEG2000 has a much Higher computational complexity than JPEG,especially for larger pictures.

Need parallelimplementation to reduce compressiontime.

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JPEG2000 Compression Scheme

WaveletTransform

Input BlockwisePartition

Codingof each block

BinaryCompressed data

Major steps of JPEG2000 image compression

• Wavelet transform uses most of the image compression time (>80%)• parallel implementation should focus on wavelet transform

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Brief Introduction to Wavelet Transform

Step 1: Horizontal wavelet transform of an image

for each rowdo 1-D wavelet transform;

end

What is 1-D wavelet transform ?

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A simple example: 1-D Haar wavelet transform

One array of image data

[1, 1]

[1, -1]

2

2

First half of the output

Second half of the output


HorizontalWavelet

Transformof Each Row

Averageof neighboringpixels

Differenceof neighboringpixels

Low-Frequencycoefficients

High-Frequencycoefficients

Low High

Low-pass filter

high-pass filter

Down-sample by 2

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Wavelet Transform

Step 2: Vertical transform of image

for each column of the new imagedo 1-D wavelet transform;

end

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HorizontalWavelet

Transformof Each Row

Low High

VerticalWavelet

Transform of Each Column

LowLow

HighLow

LowHigh

HighHigh


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Parallel Design of JPEG2000 Compression

Two Parallel Computing Architectures

Shared-Memory Multiprocessors

• Has a single address space. • Allow processors to communicate through variables stored in a

shared address space• Programming tool: openMP

Distributed-Memory Multiprocessors

• Each processor has its own memory module

• Processors communicate to each other over a high-speed network

• Programming tool: MPI (Message Passing Interface)

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Parallel Implementation of JPEG2000 Compressionon Distributed-Memory Multiprocessors

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Parallel Design of JPEG2000 Compression-DMP

Traditional Approach

• The image is first divided into n regions on rows.

• Each processor performs 1-D horizontal wavelet transform

• Then, the new image is divided into n regions on columns.

• Each processor performs 1-D vertical wavelet transform.This approach requires intensive data transmission among processors, has very high network communication cost.

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Tiling Approach

• JPEG2000 international standard supports tile-based image compression.

• A large image is divided into several tiles and each image tile is compressed independently.

P1 P2 P3

P5

P8P7

P4 P6

P9

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Choose MPI for parallel implementation of JPEG2000, because the JPEG2000 software is written in C, which supported by MPI. Basic framework is:


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Number of processors

Com

pres

sion

Tim

e (S

ec)

The picture shows the compression time using different tile size.For each tile size,processor number increases,compression time is reduced.The small tile need larger computation overhead.

Size: 32

Size: 256

Image:512x512


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• There is a jump between one process and two processes.

• When there is only one process, JPEG2000 compression is sequential

• If there are more than two processes involved in the program, Process 1 is responsible for collecting data, while the others are responsible for processing different tiles and sending processed data back to the Process 1.

Note


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Parallel Implementation of JPEG2000 Compressionon Shared-Memory Multiprocessors

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Parallel Design of JPEG2000 Compression-SMP

A problem with tile-based approach

Images compressed by JPEG, JPEG2000, and JPEG2000 with relatively small tiles.

Each tile is compressed independently, which causes discontinuity across tile edges, also called blockiness.

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• Another parallel architecture is shared-memory multiprocessors.

• The excellent price-performance ratio of Intel-based SMPs make such systems very popular in many data processing applications.

• There are also many available programming tools for shared memory processor, such as openMP and Java Threads.

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• In SMP, we do not need worry about data communication over network, because the data is in the shared memory. So there is no need for tile partitioning.

• Therefore, we can use the traditional data partitioning approach for horizontal and vertical wavelet transforms.


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• JPEG2000 image compression is implemented on a 4-processor SMP system using direct openMP.

• The speedup in wavelet transform is only about 1.6 times, which is supposed to be near 4 times.

• Why?

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It is found that the vertical wavelet transform requires more than 10 times the horizontal transform.

But we know that both vertical and horizontal transforms have the same number of operations.

vertical horizontal

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Cache Miss Problem


• In computer memory, the image data is stored line by line in a raster-scan order (from left to right, from top to bottom).

• Each continuous block of image data is brought into the cache from memory for wavelet transform.

• In horizontal wavelet transform, as the filter window is moving, the data of next transform is often available, few cache miss.

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Cache Miss Problem


• In vertical wavelet transform, the filtering is done in the vertical direction, however, the data is brought into cache in a horizontal way. So, there are very frequent cache miss.

data

filt

erin

g

Solution

Do vertical transform of several columns at the same time to make full use of the existing data in the cache. , instead of column by column Significantly reduces cache miss.

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Original Verticaltransform


Improved Verticaltransform

The vertical transform is speed upby about 10 times.

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Using the improved vertical wavelet transform, the overall speedup times of wavelet transform is now close to the number of processors.

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• Give a brief review JPEG2000 image compression.

• Discussed two approaches for parallel implementation of JPEG2000 image compression: distributed memory multiprocessor and shared memory multiprocessor.

Conclusion

Question?

Documents

Page 1 CS Department Parallel Design of JPEG2000 Image Compression Xiuzhen Huang CS Department UC Santa Barbara April 30th, 2003