A Generative Decoder That Skips Redundant Frames for Faster Video Analytics

What is it about?

ParaDeco introduces a parallel generative decoding framework for video analytics, breaking the bottleneck caused by traditional reference-dependent decoding. By independently reconstructing pseudo key frames and filtering only frames that truly matter, it enables a “decode-what-matters” workflow that eliminates over-decoding and significantly boosts throughput.

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Photo by 0xk on Unsplash

Photo by 0xk on Unsplash

Why is it important?

Although frame filtering is widely used to reduce inference cost in video analytics, current systems remain constrained by codec-level reference dependencies that force unnecessary decoding of non-selected frames. This systemic limitation has received little attention despite becoming a dominant performance bottleneck at scale. Our work introduces a new paradigm—a reference-free, frame-level parallel generative decoder—that reconstructs frames independently using compressed metadata. This approach eliminates over-decoding, offering a timely and impactful improvement for cloud-scale video analytics workloads.