Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
Covers advanced topics in watermarking, including resisting scaling and rotations, self-referenced watermarking, and types of attacks.
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Covers corrected exercises from the 2020 exam in the field of robotics, including topics such as accuracy, speed, DC motors, optimal gear ratio, dynamics of robot arms, encoders, and kinematics.
