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8/8/2019 RD-OPT & Adaptive Huffman Coding
http://slidepdf.com/reader/full/rd-opt-adaptive-huffman-coding 1/16
by
B.Nirmal
8/8/2019 RD-OPT & Adaptive Huffman Coding
http://slidepdf.com/reader/full/rd-opt-adaptive-huffman-coding 2/16
Introductiony In this project we proposed the efficient method of
image compression
y This technique is simple in implementation and
utilizes less memory
y An image of size 1024 x 1024 pixels, in which the
intensity of each pixel is an 8-bit quantity, requires 1
megabytes of storage space
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y Digital storage for image processing applications falls in to
three principal categories
1) Short-term storage for use during processing
2)Online storage for relatively fast recall
3)Archival storage, characterized by Infrequent Access
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JPEG Compressiony In lossy compression scheme,
The compression is achieved by dividing the image
in blocks of 8×8 pixels.
Applying a discrete cosine transform (DCT) on the
partitioned image.
Resulting coefficients are quantized less significant
coefficientsThe dc coefficient and ac coefficient values are
encoded separately
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Fig 1.DCT based encoder/decoder processing steps
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y Efficient quantization matrix has to be designed based
upon the rate and distortion specification
y In image compression the encoding scheme plays the
important role
y The number of bits that used to represent the coefficients
must be reduced.
y Adaptive Huffman coding is used to encode the
coefficients since it is single pass method
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RD-OPT: Algorithm for Optimizing
DCT Quantization TablesIntroduction:
y A n Efficient algorithm for constructing DCT
quantization table with optimal rate -distortiontradeoffs for a given image
y It is used to compress the image at any desired
signal to noise ratio or compressed size
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y other approach include
Psycho-visual model based quantization
Rate-distortion based quantization Stochastic Optimization quantization
y Our algorithm is based on the Rate Distortion
y The Key feature of this algorithm is optimize theQuantization table over wide range of rates anddistortions
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Bit Rate for DCT Compressiony Rate is defined as the number of bits used to represent
per pixel
Rate= Size of compressed image
No of pixels in the image
y Increasing entries in the Q tends to decrease the rate
Rn(q)=(1/64)Entropy{(f[n]//q)},R(Q) R n(Q[n]).
n=0 to 63
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RD-OPT Algorithmy
Input:An image I of W X H . Pixel range [0«..255]
yOutput:
R D-optimal DCT Quantization table Q
y Step 1: Gather DCT Statics for the image
y Step 2: use the statics to calculate R n(q) and Dn(q) for
each possible q
y Step 3: use dynamic programming to optimize R(Q) against
D(Q)
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Adaptive Huffman coding
y Huffman coding requires K nowledge of probabilities
of source sequence . It is a two pass procedure
y In Adaptive Huffman coding no need of probabilities
of source symbol. So it is a single pass procedure
y It based on statics of the symbols already encountered.
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Encoding Procedure
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Decoding Procedure
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Updating Procedure
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Example