image compression this is use to minimize the image storage

1. Image Compression
and color image processing
Chapter 5

2.  Basic definition of image compression
 Data redundancy
 Elements of information theory
 General mechanism and types of data
compression and image restoration
 Huffman coding
 Arithmetic coding
 Dictionary based coding
 Bit-plane coding
Outlines

3. ⦿ It is the art and science of reducing the
amount of data required to represent an
image.
⦿ Image compression is the process of reducing
the size of an image file without significantly
degrading its visual quality.
⦿ This is achieved by removing redundant or
unnecessary data from the image.
Image compression

4. ⦿ Driving factors behind image compression
 Storage space
 Shorten transmission time
⦿ It is essential for saving storage space,
reducing bandwidth usage, and improving
load times for websites and applications.
⦿ To achieve Compression , Reduction of
redundant data is necessary.
Original
Image compress
Compressed
Image file decompress extracted
Image file
Con’t

5. ⦿ Compression techniques are broadly classified as
Lossless and Lossy.
⦿ Lossless Compression
◾ Reduces file size without losing any data.
◾ The original data can be perfectly reconstructed from the
compressed version.
◾ It is used mainly for compressing texts, executable programs,
spreadsheets, etc..
◾ Medical imaging , Space images ,Technical drawings etc
⦿ Lossy Compression
◾ Reduces file size of an image by permanently eliminating certain
amounts of data, particularly details that are less noticeable to the
human eye.
◾ This compression is best suited for images where reduction in
file size is more critical than preserving the exact original
quality.
◾ Advantage is higher compression
◾ Web images, Photographic images
Compression techniques

6. ⦿Common lossless compression
techniques include:
◾Huffman coding
◾Arithmetic coding
◾Dictionary based coding
◾Bit-plane coding
Con’t

7. ⦿ Huffman coding is the most popular variable
length coding technique.
⦿ It is based on the frequency of occurrence
of data items and
⦿ is particularly effective when some data items
occur much more frequently than others.
⦿ Forward Pass
1. Sort probabilities per symbol
2. Combine the lowest two probabilities
3. Repeat Step2 until only two probabilities remain.

8. ⦿ Arithmetic coding is a more advanced
lossless compression technique.
⦿ Arithmetic coding represents a sequence of
symbols(such as pixel values) as a single
number in range [0,1]. This results in a
more compact representation.
⦿ Arithmetic coding yields better
compression
⦿ Entire sequence of source symbols is assigned a
single arithmetic code word
⦿ Disadvantage
 Slower than Huffman coding
 Random access is difficult

9. ⦿ Dictionary-based coding is a powerful method
for lossless data compression that relies on
identifying and encoding repeated patterns in
the input data.
⦿ Unlike statistical methods such as
Huffman and arithmetic coding, which
focus on individual symbol probabilities,
dictionary-based methods compress data
by finding and encoding repeated patterns
or substrings.
⦿ The most well known dictionary-based
algorithm is the Lempel-Ziv-Welch(LZW)
algorithm.
Dictionary-based coding

10. ⦿ Bit-plane coding is a technique used
primarily in image compression, where
the image is decomposed into a series of
binary images (bit-planes).
⦿ Each bit-plane represents a single bit
position across all pixel values in the
image.
⦿ This method can be used for both lossless
and lossy compression.
Bit-plane coding

11. ⦿ Discrete Cosine Transform (DCT): Used in
JPEG compression, it transforms the image into
a sum of cosine functions oscillating at
different frequencies.
⦿ Quantization: This process reduces the
number of colors or brightness levels in an
image, which reduces the amount of data
needed to represent the image.
⦿ JPEG (Joint Photographic Experts Group):
The most widely used lossy compression
format.
⦿ It uses DCT, quantization, and entropy coding.
Common lossy compression
techniques

12. ⦿ Lossless Compression: Best for images where
quality is paramount, such as medical imaging,
technical drawings, and images that require
further editing. (e.g., PNG)
⦿ Lossy Compression: Suitable for everyday
photos, web images, and scenarios where a
balance between quality and file size is needed.
(e.g., JPEG or WebP)
Choosing the Right Compression

13. ⦿ Telecommunications: Efficient data
transmission over limited bandwidth channels.
⦿ Digital Storage: Reduced storage requirements
for digital files.
⦿ Medical Imaging: Restoration techniques are
crucial for enhancing the quality of medical
images for accurate diagnosis.
⦿ Photography and Videography: Compression
helps in managing large image and video files,
while restoration can improve the quality of old
or degraded media.
Applications of Compression and Restoration

14. ⦿ Adobe Photoshop: Provides options for saving
images in various formats with different compression
settings.
⦿ GIMP(GNU Image Manipulation Program): is a free
and open source raster graphics editor used for
image retouching, editing, composition and more.
⦿ An open-source alternative to Photoshop with
support for various compression techniques.
⦿ ImageMagick: A command-line tool for image
manipulation and compression.
⦿ Online Services: Websites like TinyPNG and JPEG-
Optimizer offer convenient image compression
services.
Tools and Software

15. ⦿ The use of color is important in image
processing because:
⦿ Color is a powerful descriptor that simplifies
object identification and extraction.
⦿ Humans can discern thousands of color shades
and intensities, compared to about only two
dozen shades of gray..
Color Image Processing

16. ⦿ Colors are seen as variable
combinations of the primary colors of
light:
⦿ primary colors of light: red (R), green
(G), and blue (B).
⦿ The primary colors can be mixed to
produce the secondary colors:
⦿ secondary colors: magenta (red+blue),
cyan (green+blue),and yellow
(red+green).
⦿ Mixing the three primaries, or a
secondary with its opposite primary
color, produces white light.
Color Fundamentals
Figure 1: Primary and
secondary colors of light

17. ⦿ Color image processing involves the
manipulation and analysis of images that
contain color information, as opposed to
grayscale images which only contain intensity
information.
Color image processing

18.  The organization of the colors of in an image in a
specific format is called color space.
 The way in which a color is represented is called a
color model.
 Each and every image uses one of the following color
spaces for effective picture representation:
RGB (Red, Green, Blue):
⦿ The most common color space where colors are
represented as combinations of red, green, and blue
intensities.
HSV (Hue, Saturation, Value):
⦿ Represents colors in terms of hue (color type),
saturation (intensity of the color), and value
(brightness).
Color space

19. HSL (Hue, Saturation, Lightness):
⦿ Similar to HSV but with lightness instead of
value.
CMYK (Cyan, Magenta, Yellow, Key/Black):
⦿ Used in printing, representing colors as
combinations of cyan, magenta, yellow, and
black.
YUV/YIQ:
⦿ Used in video systems, separating luminance
(Y) from chrominance (UV or IQ).
Con’t

20. ⦿ Medical Imaging: Enhancing and analyzing
medical images for diagnosis.
⦿ Remote Sensing: Processing satellite or aerial
images for environmental monitoring.
⦿ Computer Vision: Enabling machines to
interpret and understand visual information.
⦿ Digital Photography: Enhancing and editing
photos for better visual appeal.
Applications of Color Image Processing

21. ⦿ OpenCV: A powerful library for image
processing tasks, including color image
processing.
⦿ MATLAB: Provides extensive tools for image
processing and analysis.
⦿ Python Libraries: Such as PIL/Pillow, scikit-
image, and NumPy for image manipulation.
Tools and Libraries