Stefano MARANO | Information Coding and Compression
Stefano MARANO Information Coding and Compression
cod. 0622700032
INFORMATION CODING AND COMPRESSION
| 0622700032 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2020/2021 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2017 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/03 | 3 | 24 | LESSONS | |
| ING-INF/03 | 3 | 24 | EXERCISES |
| Objectives | |
|---|---|
| THIS COURSE PRESENTS THE BASIC ELEMENTS OF: - EFFECTIVE REPRESENTATION OF INFORMATION IN DIGITAL SYSTEMS (SOURCE CODING) - RELIABLE TRANSMISSION IN DIGITAL COMMUNICATION SYSTEM (CHANNEL CODING) - ANALYSIS AND IMPLEMENTATION OF SOURCE AND CHANNEL ENCODERS/DECODERS KNOWLEDGE AND UNDERSTANDING: ABILITY IN ANALYSIS AND MODELING SOURCES OF INFORMATION AND COMMUNICATION CHANNELS APPLIED KNOWLEDGE AND UNDERSTANDING: DESIGN OF SOURCE AND CHANNEL CODES |
| Prerequisites | |
|---|---|
| SKILLS ARE REQUIRED IN: MATHEMATICS, PROBABILITY, AND PROGRAMMING. |
| Contents | |
|---|---|
| ELEMENTS OF INFORMATION THEORY (7/5) ENTROPY. MUTUAL INFORMATION AND DIVERGENCE. LOG-SUM INEQUALITY AND ITS IMPLICATIONS. AEP. DATA PROCESSING INEQUALITY. CHANNEL CODING (7/5) THE CONCEPT OF CAPACITY. CAPACITY OF BSC. FANO'S INEQUALITY. SECOND SHANNON THEOREM. CAPACITY OF THE GAUSSIAN CHANNEL WITH POWER CONSTRAINT. BLOCK CHANNEL CODES. CONVOLUTIONAL CODES. TURBO CODES. CODES OF THE LDPC FAMILY. COMPUTER IMPLEMENTATION OF THE PRESENTED CODES. LOSSLESS SOURCE CODING (7/5) TAXONOMY OF CODES (NON-SINGULAR, PREFIX-FEE, UNIQUELY DECODABLE). SOURCE CODE THEOREMS. HUFFMAN CODES. LEMPEL-ZIV CODES. COMPUTER IMPLEMENTATION OF THE PRESENTED CODES. LOSSY SOURCE CODING (7/5) SCALAR QUANTIZER. RATE DISTORTION TRADE OFF. UNIFORM AND NON-UNIFORM QUANTIZERS. PRINCIPLES OF OPTIMAL QUANTIZATION “NEAREST NEIGHBOR” AND CENTROID RULES. LLOYD & MAX ALGORITHM. TRANSFORM CODING. COMMONLY USED ALGORITHM FOR LOSSY CODING: JPEG STANDARD. MPEG STANDARD. COMPUTER IMPLEMENTATION OF THE PRESENTED CODEC FOR IMAGE/AUDIO/VIDEO. BRIEF PRESENTATION OF COMPRESSED SENSING CONCEPTS. |
| Teaching Methods | |
|---|---|
| THE COURSE OFFERS THEORETICAL AND PRACTICAL LESSONS (COMPUTER IMPLEMENTATION OF ENCODERS/DECODERS) ON SOURCE AND CHANNEL CODING |
| Verification of learning | |
|---|---|
| THE STUDENT WILL PRESENT A COMPUTER PROJECT (IMPLEMENTATION AND ANALYSIS OF SOURCE AND CHANNEL ENCODERS/DECODERS) ALONG WITH A BRIEF ORAL OR WRITTEN REPORT. THE EVALUATION WILL BE BASED ON PROJECT RESULTS, CORRECTNESS AND EFFICACY OF THE FORMULATION, CLARITY OF EXPOSITION, DEPTH OF UNDERSTANDING, DEVELOPED SKILLS, CRITICAL ANALYSIS AND GENERAL VISION OF THE TOPIC. |
| Texts | |
|---|---|
| T. M. COVER, J. A. THOMAS, ELEMENTS OF INFORMATION THEORY, JOHN WILEY & SONS, 1991. A. GERSHO, R. M. GRAY, VECTOR QUANTIZATION AND SIGNAL COMPRESSION, SPRINGER, 1992 |
| More Information | |
|---|---|
| THE COURSE LANGUAGE IS: ITALIAN. |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2022-05-23]
