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5700_2 (Color Image Processing (Color Models and Color Conversion (YIQ…
5700_2
Color Image Processing
Color Fundamentals
Pseudo-color image processing
Full-color image processing
Primary and Secondary Colors
Color Models and Color Conversion
RGB Model
CMY Model
CMYK Model
YIQ Color Model
Used for color TV broadcasting
HSI Color Model
HSV Color Space
Color Table
The advantages :
Image compression in true color image.
Processing Speed.
Hardware Support.
True color
Pseudo-Color
Intensity Slicing
General Gray level to color transformation
Color Image Processing Techniques
Noise in Color Images
Color Image Smoothing
averaging mask
Color Transformation
Deal With Color Vector
Per-color-component processing
Vector-based processing
Color Image sharpening
Laplacian
Denoising of Color Image
Multichannel nonlocal means fusion
Image Compression
Fundamentals
Why Compression
Compression Types
lossless compression
Lossy compression
Compression Formats
data redundancies
coding redundancy
compression ratio
relative data redundancy
average number of bits
interpixel redundancy
highly correlated.
mapping
psychovisual redundancy
Quantisation
Improved grey-scale (IGS)
Bit-plane Representation
Fidelity Criteria
Objective delity criteria
root-mean-square error
mean square signal-to-noise ratio
Subjective delity criteria
based on rating
Image Compression Model
Source Encoder
removes input redundancies
mapper
quantizer(irreversible)
symbol encoder
Source Decoder
symbol decoder
inverse mapper.
Channel encoder
increases the noise immunity
Compression algorithm
Lossless Compression:
reduce interpixel redundancy
eliminate coding redundancy.
Hu ffman Encoding
uniquely decodable
Need to store symbol table
Run-Length Coding
Horizontal/vertical
Arithmetic coding
Lempel-Ziv-Welch (LZW) coding
Lossless Predictive Coding
predictor
Lossy Compression
Lossy Schemes
Image difference is still recognizable.
30:1 Compression ratio
Image is virtually" indistinguishable from original
20:1 - 10:1 Compression ratio
quantization
Lossy Predictive Coding
Transform coding
Discrete Cosine Transform (DCT)
Subimage size (n=?)
JPEG Compression Model
2D DCT
Quantization
Uniform Quantization:
Non-uniform Quantization
Zig-Zag Scan
DPCM on DC Components
RLE on AC Components
Hybrid Method
Wavelet Methods
Image Deblurring and Its Applications
Deduce Kernel From Saturated Regions
Separable Blur Kernel
Trajectory
Intensity
Point Spread Function
Pre-analysis: One Component Degraded Kernel
Non-uniform Kernel Initialization
Detect Saturated Region Automatically : Laplacian of Gaussian
Separable Kernel Model for Valid Saturated Region
Extract trajectory using triangle mesh
Intensity and PSF Estimation
Randomized Kernel Optimization
Random Trajectory Perturbation
RTP: Hierarchical Perturbation
RTP: E ectiveness of Perturbation
Deblurring Optimization with New Initial Kernel
Deblurring Optimization with Initial Kernel
Randomized Kernel Optimization
Fundamental of Digital
Video Coding
Digital Video Representation
Application scenarios of video coding
transmission
storage
Theoretical basis of video coding
Remove spatial and temporal redundancy
Psychophysical redundancy
Entropy coding
Digital video formats
4:4:4 sampling format
4:2:2 sampling format
4:2:0 sampling format
Current status of digital video/image coding standards
Standard activities
Brief description of standards