MULTIMEDIA AND LABORATORY

Module LABORATORY

Become an expert in multimedia systems concerning images and video, and improve the skills related to multimedia system programming.

General learning objectives in terms of expected learning outcomes.

1. Knowledge and understanding: The aim of the course is to acquire knowledge that will enable the student to understand the theoretical and physical mechanisms behind the signal theory and underlying the human visual system, the formation and processing of video and digital images, enhancing their visual quality.
2. Ability to apply knowledge and understanding: the student will acquire the skills needed to acquire, edit, compress and save images and video signal. 
3. Making judgments: Through examples in the classroom, the student will be put into the condition of understanding whether the solutions offered by him meet a certain degree of quality.
4. Communication skills: The student will acquire the necessary communication skills and technical language skills in the multimedia field.
5. Learning Skills: The aim of the course is to provide the student with the necessary theoretical and practical methodologies to deal with and solve new problems that arise during a work activity. To this end, several topics will be addressed in lesson by involving the student in the search for possible solutions to real problems.

Classroom lessons

Seminars

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Access to the teaching materials provided by the instructor is available on MS Teams, in the "Multimedia e Laboratorio" Team, code: 28vtwgp.

All communications will take place through the official Telegram channel of the course, so students are requested to join it: https://t.me/+S6Yl1xViNCtJCRmE.

Evaluating the quality of an image. Objective and subjective criteria. PSNR, SSIM, Delta E in CIE L* a* b*.

Raster and vector image formats. Image formats: BMP, PNG, TIFF, GIF. Compression and Coding: Huffman, Golomb, Arithmetic.
LZW, differential, RLE, code-based encodings, based on the symbols on the bit plane. Encrypt using the transform.
Transformed Haar, Fourier, DCT.
The mathematical morphology applied to the images.
The mathematical morphology applied to the images in gray scale.
restoring images. Noise patterns
Arithmetic, geometric, harmonic and harmonic media filters. Median filter, minimum, maximum, midpoint, alpha-trimmed. Adaptive Filters. Periodic noise. Removing noise in the frequency domain. Notch filter. Wiener filter.
Filtering in Spatial Domain. Edge detector. Canny Algorithm. Filtering in the frequency domain. Stress filtering. Homomorphic filtering. Hough transformed.
Segmenting Images

Examples of coding using Matlab

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Learning assessment may also be carried out on line, should the conditions require it.

Digital Image Processing, Third Edition, Rafael C. Gonzalez, Richard E. Woods, Ediz. Pearson, Prentice Hall 

In order to take the exam, in accordance with the regulations, it is MANDATORY to register on the Smart Edu portal and on any other platform, as MS Forms, required by the instructor to optimize logistics.

There will be some mid-term exams. A positive result in these exams will exempt students from taking the final exam during the official examination periods

The examination is held in italian language according the rules described in the italian version of this section.

Erasmus students and other non-italian speakers may ask to take an oral exam.

Students with disabilities and/or learning disorders (DSA) must contact the instructor and the CInAP representative at DMI well in advance of the exam date to inform them of their intention to take the exam with the appropriate compensatory measures.

For the assignment of grades for individual assessments, the following criteria are typically followed:

• Fail: The student has not acquired the basic concepts and is unable to complete the exercises.
• 18-23: The student demonstrates a minimal mastery of the fundamental concepts; their ability to present and connect content is modest, and they can solve simple exercises.
• 24-27: The student shows a good grasp of the course content; their ability to present and connect the content is good, and they solve exercises with few errors.
• 28-30 with honors: The student has acquired all course content and can present them comprehensively with a critical perspective; they solve exercises completely and without errors

The assessment of learning may also be conducted remotely if the conditions require it.

Learning will be assessed by completing a software project agreed upon with the instructor.

The assessment of learning may also be conducted remotely if the conditions require it.

Random noise in images:

What is random noise in images? What can it be introduced by?

Let P be a probability distribution with the law P(x)=0.10 for x=0; P(x)=0.25 for x=255; P(x)=0 otherwise. Where x is an 8-bit (integer) luminance value. What is the name of the noise that follows this probability distribution? Discuss the significance of the distribution described.

What is the contraharmonic averaging filter? How is it defined?

Can the contraharmonic averaging filter be used to attenuate the aforementioned P-distribution noise? If yes, explain how. If not, propose another type of filtering.

Morphological operators:

What is the structuring element in mathematical morphology applied to images?

What is the Bottom-hat morphological operator used for?

The Closure operator is used in the definition of the Bottom-hat operation. How is this Closing operation defined? What are its effects?

Indicate at least one mathematical property of the Closure operator.