MULTIMEDIA E LABORATORIO

Academic Year 2021/2022 - 1° Year - Curriculum Data Science

Learning Objectives

• Multimedia

  Become an expert in multimedia systems: images, audio and video.

  General learning objectives in terms of expected learning outcomes.

  Knowledge and understanding: The aim of the course is to acquire knowledge that will enable the student to understand the theoretical and physical mechanisms underlying the human visual system, the formation and processing of sound, video and digital images, enhancing the visual quality of digital images and audio quality.
  Ability to apply knowledge and understanding: the student will acquire the skills needed to acquire, edit, compress and save a viedeo audio signal. Particularly a part of the course will be related to the study of Matlab software to apply such theoretical knowledge.
  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.
  Communication skills: The student will acquire the necessary communication skills and technical language skills in the multimedia field.
  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.

• Laboratory

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

  General learning objectives in terms of expected learning outcomes.

  Knowledge and understanding: The aim of the course is to acquire knowledge that will enable the student to understand the theoretical and physical mechanisms underlying the human visual system, the formation and processing of sound, video and digital images, enhancing the visual quality of digital images and audio quality.
  Ability to apply knowledge and understanding: the student will acquire the skills needed to acquire, edit, compress and save a viedeo audio signal. Particularly a part of the course will be related to the study of Matlab software to apply such theoretical knowledge.
  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.
  Communication skills: The student will acquire the necessary communication skills and technical language skills in the multimedia field.
  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.

Course Structure

• Multimedia

  Classroom lessons

  Laboratory lessons

• Laboratory

  Laboratory lessons

Detailed Course Content

• Multimedia

  Segnali: cenni sulle onde, serie di Fourier ed esercizi
  Segnali: tipi di segnale, campionamento, teoremi di shannon per il campionamento e la ricostruzione, quantizzazione uniforme e non uniforme, SQNR e RMS
  Segnali: dithering, tipologie di dithering: random, ordered, error diffusion; algoritmo di Floyd-Steinberg, algoritmo di Jarvis
  Segnali: Trasformate discrete, metodo di costruzione, trasformata di Haar, di Walsh/Hadamard e di Fourier
  Segnali: Laboratorio in MATLAB sugli argomenti trattati

  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
  
  Digital Video: Aspect Ratio; Resolution; Interlacing; Analog and digital transmission and recording formats.
  Digital Video: Analog-Digital Conversion; Part II - Common Registry, Artifact and Drop Errors; Part III - Projections of 3D and 2D space, CAHV model, Matlab lab on the projections, introduction to the most common motion areas.
  Digital Video: Matlab Laboratory: Comparison of Ideal and Empirical Motion Fields, Calculation and Visual Analysis of Generic Motion Fields. 4-Parameter and 6-Parametric Camera Movement Models. Motion Estimator Criterion: Displaced Frame Difference. Block matching algorithms: exhaustive and three-step search. Feature matching algorithms: FAST.
  Digital Video: Stabilization. Digital Stabilization Systems: Motion Vector Integration, Frame Position Smoothing and Kalman Filtering; Examples of image deformation and chromatic stabilization. Matlab Laboratory: motion detection with background subtraction; application example of the Kalman filter; example of video stabilization application using FAST algorithm.
  Digital Video: MPEG-1, MPEG-2, MPEG-4, H.264 video formats. 3D Scan - laser scanning (triangulation, structured light, flight time, photographs): hardware, methods and examples.

   

  
  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.

• Laboratory

  The laboratory module focuses on the implementation of the main algorithms for multimedia processing, which are studied in the theoretical module.

  The employed programming language are Python and Matlab.

Textbook Information

• Multimedia

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

  Video Processing and Communications, Wang, Osternmann, Zhang, Prentice Hall, Pearson Education, ISBN: 0-13-017547-1

• Laboratory

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

  Audio e multimedia 3 ed., di Lombardo, Valle, Apogeo ISBN: 9788850327621

  Video Processing and Communications, Wang, Osternmann, Zhang, Prentice Hall, Pearson Education, ISBN: 0-13-017547-1