Tutorial #1

Part I: Overview of Learned Image/Video Coding (by Prof. Peng; 15 mins)

• Introduction to end-to-end learned image and video coding
• The rate-distortion performance of SOTA learned image/video codecs
• Standardization activities on neural image/video coding in JPEG and MPEG

Part II: End-to-End Learned Image Coding (by Prof. Sun; 70 mins)

• Elements of end-to-end learned image coding
• Review of a few notable tool features (e.g. fast context models)
• Network pruning and quantization for learned image codecs
• Implicit Neural Representation (INR)-based image coding systems
• Real-time implementation of learned image codecs

Coffee Break (20 mins)

Part III: End-to-End Learned Video Coding (by Prof. Peng; 60 mins)

• End-to-end learned video coding frameworks: residual coding, conditional coding, and conditional residual coding
• Review of some notable systems
• The explicit, implicit, and hybrid temporal buffering strategies
• The rate-distortion-complexity trade-offs from the perspectives of coding frameworks and buffering strategies
• Network quantization for learned video codecs

Part IV: Practical Implementation (30 minutes)

• Emerging learned coding techniques for 3D/4D Gaussian Splatting and multi-modal large language modals
• Open issues and concluding remarks