# Digital image processing image compression

• JPEG: the Joint Photographic Experts Group.
• It uses 24-bit color for RGB images.
• Advantage: it provides true color while
compressing image files more than the lossless
compression methods.
• Disadvantage: it must use a lossy compression
method, so some image data is lost.
• The JPEG method is based on the fact that
humans are much more aware of small changes in
brightness (luminance) than to small changes in
color or large changes in color or brightness.

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Nguyễn Công Phương
DIGITAL IMAGE PROCESSING
Image Compression
Contents
I. Introduction to Image Processing & Matlab
II. Image Acquisition, Types, & File I/O
III. Image Arithmetic
IV. Affine & Logical Operations, Distortions, & Noise in Images
V. Image Transform
VI. Spatial & Frequency Domain Filter Design
VII. Image Restoration & Blind Deconvolution
VIII. Image Compression
IX. Edge Detection
X. Binary Image Processing
XI. Image Encryption & Watermarking
XII. Image Classification & Segmentation
XIII. Image – Based Object Tracking
XIV. Face Recognition
XV. Soft Computing in Image Processing
sites.google.com/site/ncpdhbkhn 2
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 3
Introduction
• Image compression: algorithmic techniques that
can reduce the storage requirements of the image
and, at the same time, retain the image
information content.
• Aspects of image properties that can be used for
compression:
– The interpixel information variation is only significant
at edges of any type, whereas most of the image
information content remains a slowly changing
variable.
– Our eyes are less sensitive to color changes and are
much more sensitive to intensity.
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Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 5
Image Compression Steps
1. Specification: This implies specifying the rate (bits
available) and distortion (tolerable error) parameters for
the target image.
2. Classification: This implies dividing the image data into
various classes, based on their importance. Usually, some
type of compression transform is utilized in this step to
associate the important features with the most important
class of information to be kept in the process of
compression.
3. Bit allocation: This implies dividing the available bit
budget among these classes such that the distortion is a
minimum.
4. Quantization: This refers to quantizing each class
separately using the bit allocation information derived in
step 3.
5. Encoding: This corresponds to encoding each class
separately using an entropy coder and write to the file.
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Image Decompression Steps
1. Decoding: Read in the quantized data from the
file, using an entropy decoder (reverse of step 5).
2. Dequantizing: This refers to normalizing the
quantized values (reverse of steps 4 and 3). This
also includes any padding or addition of missing
values due to the quantization process.
3. Rebuilding: This involves the inverse transform
or inverse classification of the normalized data
into image pixels, essentially rebuilding the
image (reverse of step 2).
sites.google.com/site/ncpdhbkhn 7
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 8
Error Metrics
• MSE (mean square error): a lower value for
MSE means less error.
• PSNR (peak signal-to-noise ratio): a higher
value of PSNR is good.
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M N
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25520logPSNR
MSE
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 10
Classifying Image Data
• In this step, usually the image is represented as a two-dimensional array of
coefficients, where each coefficient represents the brightness level at that
point.
• From a high-level perspective, one cannot differentiate between
coefficients as more important ones and lesser important ones, but
• Most natural images have smooth color variations, with the fine details
being represented as sharp edges between the smooth variations.
• Technically, the smooth variations in color can be termed low-frequency
variations and the sharp variations, high-frequency variations.
• The low-frequency components (smooth variations) constitute the base of
an image, and the high-frequency components (the edges that give the
detail) add upon them to refine the image, thereby giving a detailed image.
• Hence, the smooth variations require more importance than the details.
• Separating the smooth variations and details of the image can be done in
many ways. Two well-known image transforms used for this purpose are
the discrete cosine transform (DCT) and the discrete wavelet transform
(DWT).
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Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 12
Bit Allocation
• Bit allocation: the first step in compressing an image is to segregate
the image data into different classes. Depending on the importance
of the data it contains, each class is allocated a portion of the total
bit budget such that the compressed image has the minimum
possible distortion.
• Steps:
1. Initially, all classes are allocated a predefined maximum number of
bits.
2. For each class, one bit is reduced from its quota of allocated bits, and
the distortion due to the reduction of that one bit is calculated.
3. Of all the classes, the class with minimum distortion for a reduction
of one bit is noted, and one bit is reduced from its quota of bits.
4. The total distortion for all classes D is calculated.
5. The total rate for all the classes is calculated as R = P(i)×B(i), where
P is the probability and B is the bit allocation for each class.
6. Compare the target rate and distortion specifications with the values
obtained in steps 4 and 5. If not optimal, go to step 2.
sites.google.com/site/ncpdhbkhn 13
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 14
Quantization
• Quantization: the process of approximating the continuous set of
values in the image data with a finite (preferably a much smaller) set
of values.
• The input to a quantizer is the original data, and the output is always
one among a finite number of levels.
• The quantizer is a function whose output values are discrete, and
usually finite. Obviously, this is a process of approximation, and a
good quantizer is one that represents the original signal with
minimum loss or distortion.
• There are two types of quantization: scalar quantization and vector
quantization.
– In scalar quantization, each input symbol is treated separately in
producing the output.
– In vector quantization, the input symbols are clubbed together in
groups called vectors, and processed to give the output. This clubbing
of data and treating them as a single unit increases the optimality of the
vector quantizer, but at the cost of increased computational complexity.
sites.google.com/site/ncpdhbkhn 15
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 16
Entropy Coding
• After the data has been quantized into a finite set of
values, it can be encoded using an entropy coder to give
additional compression.
• Entropy means the amount of information present in the
data, and an entropy coder encodes the given set of
symbols with the minimum number of bits required to
represent them.
• Two of the most popular entropy coding schemes are
Huffman coding and arithmetic coding.
• These techniques allocate variable-length codes for
specific symbols (coefficient values in our case) in a set
of values to be encoded.
• This saves even more space in terms of storage, thus
adding to the compression.
sites.google.com/site/ncpdhbkhn 17
Image Restoration
and Blind Deconvolution
1. Introduction
2. Image Compression – Decompression Steps
3. Error Metrics
4. Classifying Image Data
5. Bit Allocation
6. Quantization
7. Entropy Coding
8. JPEG Compression
sites.google.com/site/ncpdhbkhn 18
JPEG Compression
• JPEG: the Joint Photographic Experts Group.
• It uses 24-bit color for RGB images.
• Advantage: it provides true color while
compressing image files more than the lossless
compression methods.
• Disadvantage: it must use a lossy compression
method, so some image data is lost.
• The JPEG method is based on the fact that
humans are much more aware of small changes in
brightness (luminance) than to small changes in
color or large changes in color or brightness.
sites.google.com/site/ncpdhbkhn 19

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