Generative Video Compression via Pivot Image Reconstruction
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Solution Overview
Problem
Current video compression techniques struggle to balance file size reduction with maintaining video fidelity and latency, especially in streaming media over the internet, which consumes significant computing resources and network bandwidth.
Innovation Solution
The use of generative machine learning models to reconstruct videos based on pivot images and corresponding descriptors, allowing for efficient compression while preserving key concepts and fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video compression techniques are used to reduce file size, then the amount of data transmitted is reduced, but video fidelity and quality deteriorate
Solution Approach 1:
The patent extracts only the most essential and informative frames (pivot images) from the video sequence, rather than compressing all frames. This selective extraction reduces data quantity while preserving key visual information that maintains perceived video fidelity.
Solution Approach 2:
The patent creates compressed video representations by copying only critical pivot images and their descriptors, then reconstructing the video from these copies at the receiving end. This allows significant data reduction while maintaining essential video content and quality.
2Manufacturing precision
If more computing resources are allocated to video compression and transmission, then video quality and fidelity can be maintained, but network bandwidth and energy consumption increase
Solution Approach 1:
The patent removes unnecessary video data by extracting only pivot images that contain key information, significantly reducing the energy required for transmission and processing while maintaining video fidelity through intelligent selection of essential frames.
Solution Approach 2:
The patent transforms video data from a continuous stream into discrete pivot images with associated descriptors, changing the data representation parameters to reduce transmission energy while preserving quality through the generative reconstruction process.
3Loss of time
If traditional compression methods are used to reduce latency, then transmission time is reduced, but video quality and conceptual information are lost
Solution Approach 1:
The patent extracts pivot images that specifically capture key concepts and important moments in the video, ensuring that transmission latency is reduced while preserving essential information content through intelligent frame selection based on object detection and change analysis.
Solution Approach 2:
The patent performs preliminary analysis of video frames using object detection models to identify and select pivot images containing key concepts before transmission. This preliminary action ensures that only essential information is transmitted, reducing latency while maintaining conceptual integrity.
4Manufacturing precision
If all video frames are transmitted to maintain quality, then video fidelity is preserved, but network bandwidth and data transmission requirements increase
Solution Approach 1:
The patent extracts a minimal set of pivot images that represent the essential content of the video, dramatically reducing network bandwidth consumption while maintaining video quality through the generative reconstruction of intermediate frames from these key images.
Data Source
AI summary
Various embodiments of the technology described herein relate to compression of video data, including selecting a pivot image from a video including a plurality of images and causing a first machine learning model to generate a descriptor of the pivot image, where the descriptor includes a language description associated with the pivot image. In one example, the pivot image and the descriptor are provided to a decoder for reconstruction of the video. In an embodiment, the decoder includes a generative machine learning model that takes as an input the pivot image and the descriptor. The decoder uses the pivot image to generate an image based at least in part on the descriptor. The image is combined with other images generated by the generative machine learning model to reconstruct the video.