Explore the fundamentals of lossless data compression in this 21-minute talk by Lawrence Esswood from Churchill CompSci Talks. Delve into the importance of compression algorithms in managing large quantities of data and reducing infrastructure load. Learn about basic compression theory and its practical applications. Discover Arithmetic Coding, a technique for encoding symbols based on source distribution, and understand Prediction By Partial Matching, a method for estimating distribution to create a complete compression algorithm. Gain insights into this active area of research and its significance in modern data management and transportation.
Overview
Syllabus
Methods for Lossless Data Compression
Taught by
Churchill CompSci Talks
Related Courses
-
Data Compression I - Course Introduction and Lossless Compression Basics - Lecture 1
-
Data Compression Explained - Lossy and Lossless Techniques
-
Arithmetic Coding - Stanford EE274 Data Compression I Lecture 6
-
Context-based Arithmetic Coding and LLM Compression - Stanford EE274 Lecture 9
-
Data Compression With Machine Learning
-
The Unreasonable Effectiveness of JPEG: A Signal Processing Approach
Reviews
-
M.SUBHEKSHANA THILAGAMethods for Lossless Data Compression offer a comprehensive overview of techniques that efficiently reduce file sizes without data loss, making them essential for storage and transmission. The text clearly explains algorithms like Huffman coding, LZ77, and arithmetic coding, alongside their practical applications in fields such as software, imaging, and communication systems. It provides a solid understanding of the trade-offs involved in choosing the right compression technique, balancing speed, efficiency, and data integrity. Overall, it's a valuable resource for both beginners and experienced professionals in data science.
