Human visual system and image perception; monochrome and colour vision models; image
digitization, display and storage; 2 ‐ D signals and systems; image transforms ‐ 2D DFT, DCT, KLT,
Harr transform and discrete wavelet transform; image enhancement: histogram processing, spatial ‐
filtering, frequency ‐ domain filtering; image restoration: linear degradation model, inverse filtering,
Wiener filtering; image compression: lossy and lossless compression, image compression standards,

Basic counting principle: Pigeonhole principle, inclusion - exclusion principle, recurrence relations, generating functions. Fundamentals of logic, set theory, language, and finite state machines.

Undirected and direct graphs, modelling with graphs, trial and cycle- Eulerian trial and Hamilton cycle, connectivity and trees. Graph algorithms: BFS, DFS, shortest path, optimal spanning trees, matching, job assignment problem, optimal transportation through flows in networks

Big data analytics: Introduction to spark 2.0 & tensor flow, tools to assess the quality of big data analytics. Mini project on a topic related with data modeling.