ENGINEERING TRIPOS PART IIB – 2012/2013

Module 4F8 - Image Processing and Image Coding

AIMS

Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. The module will be split into two courses of 8 lectures each: Image Processing, and Image Coding. Lectures are supported by computer demonstrations. There will be one examples sheet for each of the two 8-lecture sections.

LECTURE SYLLABUS

Image processing (8L, Dr J Lasenby)

This course covers the following topics, relevant to most aspects of image processing:

1. Two-dimensional linear system theory, as applied to discretely sampled systems:
   • The continuous 2D Fourier transform and its properties
   • Digitisation, sampling, aliasing and quantisation
   • The discrete 2D Fourier transform (DFT)
     
   
   
2. 2D Digital Filters and Filter Design
   • Zero phase filters
   • Ideal 2D filters: rectangular and bandpass
   • Filter design: the window method
   
   
3. Image Deconvolution
   • Deconvolution of noiseless images -- the inverse filter
   • The Wiener filter (conventional and Bayesian derivations)
   • Maximum Entropy deconvolution
   
   
4. Image Enhancement
   • Contrast enhancement
   • Histogram equalisation
   • Median filtering

Image coding (8L, Dr N.G. Kingsbury)

This course concentrates on video data compression techniques, and covers the following topics:

1. Characteristics of the human visual system which are important for data compression, such as spatial and temporal frequency sensitivities and distortion masking phenomena.
2. Block transforms (including the discrete cosine transform) and overlapped transforms, to provide good energy compaction of typical images.
3. Optimal quantisation techniques for coding the transform coefficients to provide maximum data compression.

OBJECTIVES

On completion of the module, students should:

• Understand the main elements of 2-dimensional linear system theory;
• Be able to design linear spatial filters for a variety of applications (smoothing etc);
• Understand techniques for the restoration and enhancement of degraded images;
• Be familiar with the main characteristics of the human visual system with particular reference to subjective criteria for image data compression;
• Understand techniques for image coding using transform methods including the Discrete Cosine Transform (as used in the JPEG coding standard) and overlapped transforms;
• Understand methods for coding transform coefficients to provide maximum data compression.

REFERENCES

Please see the Booklist for Group F Courses for references for this module.

Last updated: June 2012