Simulation and analysis of different DCT techniques on MATLAB (presented in a Malaysian conference)

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Simulation & Analysis of Different DCT Techniques for
Image Compression on MATLAB
Youness Lahdili
Supervised by: Dr Afandi Ahmad
VLSI Architecture and Systems Design (VASYD) Research Laboratory
Microelectronics and Nanotechnology - Shamsuddin Research Centre (MiNT-SRC), University Tun Hussein Onn Malaysia (UTHM)
Problem Statement
Digital video compression/decompression algorithms
(codecs) are at the heart of many modern video
products (smartphones, digital cameras, HD TV, etc...)
Objectives of Simulation
“Transform” Part
Implementation of 1D-DCT Computing
Implementation of 1D-DCT Computing (via Chen)
Scope of Simulation
Implementation of 1D-DCT Computing (via Loeffler)
Results & Conclusion
Contact Information
Available
Storage Space
Available
Bandwidth
Images on
Internet
Images on
Media Storages
Amount of Existing
Real Images
Solution: use JPEG-like compression algorithms
These algorithms can be
Resource
Exhausting
Unfit for FPGA Protected by
Proprietary Patents
Slow
Design a rapid DCT algorithm on:
MATLAB® Validation Software Console
Set recommendations on best design practices
Elaborate novel algorithms that are patent-free
Define the JPEG technical limitations
Produce full JPEG compression based on our DCT module
Background of Simulation
1 – Color Space
Transformation 
2 – Chroma Subsampling 
3 – 2D-DCT
Computing 
where 
 1D-DCT
Transform
 Practical 2D-DCT
Computing
There are some existing methods to find 1D-DCT:
Directly from DCT
algebraic transform
DCT butterfly
factorization
Fixed-point
From FFT
algorithms
Floating-point
Loeffler,
Arai ...
Chen Algorithm
where:
It can be rewritten in matrix form, and
further reduced to become :
Butterfly Diagram  (visualization of transform parts that can be parallelizable)
I implemented these two essential blocks in
MATLAB® so to get a concrete framework and to
deploy it as executable (see demo..)
Abstraction of Simulation Algorithm
• To realize the DCT in MATLAB® we used
(1) Chen, (2) Loeffler, & (3) Multiplying with 8x8
DCT basis pattern and we compared their
performances.
• For the Quantizer, we complied with the JPEG
recommendations (Two different Quantization
masks for Luma and for Chroma)
Implementation of 1D-DCT Computing (via pattern)
Outcome after Execution of Simulation (via Chen)
(1) Chen
(2) Loeffler
(3) DCT basis pattern
• We have successfully coded the fundamental DCT stage in
MATLAB® format with optimal outcome.
• RLE, Entropy & Motion Estimation can be supplemented to lead
to a Motion-JPEG codec (ISO/IEC 29199-3).
• There is still room for improvement of our DCT unit, if we omit
to compute the last 3 or 4 rows of the macroblock, as they
would be trunkated to “0” anyway. (i.e: it is called pruning)
• The DCT algorithm we designed, has proven to be feasible in
MATLAB® environment, awaiting to be ported to FPGA.
it will deliver :
※ best performance
※ smaller footprint
※ minimal image
degradation &
※ faster execution
Time Elapsed MSE
119.2969
120.7511
120.4517
Dr Afandi Bin Ahmad
afandia@uthm.edu.my
Youness Lahdili
GE150076@siswa.uthm.edu.my