The field of video editing and neural compression is moving towards more efficient and effective methods for compressing and editing video content. Recent work has focused on developing holistic approaches to instructional video editing, improving the quality of edited videos, and enhancing the overall viewing experience. The use of neural compressors and lattice coding has shown promise in achieving optimal rate-distortion-perception tradeoffs. Additionally, the development of new benchmark datasets and evaluation metrics has facilitated progress in shot sequence ordering and cinematology-inspired computing methods. Notable papers include:

• Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff, which proposes low-complexity neural compressors using lattice coding and shared randomness.
• InstructVEdit, which introduces a full-cycle instructional video editing approach that achieves state-of-the-art performance.
• InsViE-1M, which presents a high-quality instruction-based video editing dataset and a multi-stage learning strategy for training editing models.