Lossy Video Encoding Neural Network Latent Compression
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Solution Overview
Problem
Existing lossy image and video compression techniques face challenges in achieving efficient compression while maintaining acceptable image or video quality, particularly with the increasing demand for higher resolution content which strains communications networks and increases energy use.
Innovation Solution
A method for lossy video encoding, transmission, and decoding that involves using multiple trained neural networks for encoding and decoding latent representations, with quantization and entropy encoding processes applied to reduce data quantity while minimizing noticeable loss to the human visual system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression is used to maintain all original information, then image or video quality is preserved, but data quantity reduction is limited and network strain increases
Solution Approach 1:
The patent extracts and removes information that is imperceptible to the human visual system during compression. The neural network identifies and discards redundant or visually insignificant data components, achieving substantial data reduction while preserving perceptual quality. This selective extraction resolves the contradiction by removing unnecessary information rather than retaining all original data.
Solution Approach 2:
The patent transforms image/video data into a latent representation space where compression operations are performed. By changing the parameter space from raw pixel values to learned latent features, the system achieves more efficient compression ratios while maintaining reconstructable quality through the inverse transformation process.
2Reliability
If higher resolution content is transmitted to meet user demand, then image or video quality is improved, but network energy consumption increases
Solution Approach 1:
The patent extracts only the essential visual information needed to maintain perceived quality, removing redundant high-resolution data that would consume excessive network bandwidth and energy. The neural network identifies and eliminates imperceptible details, enabling efficient transmission of high-quality content with reduced energy consumption.
Solution Approach 2:
The patent applies partial compression by retaining only the necessary portion of visual information for acceptable quality. Rather than transmitting complete high-resolution data, the system transmits a compressed representation that provides sufficient visual fidelity for most applications, reducing network energy usage while meeting quality requirements.
3Quantity of substance
If traditional compression techniques remove imperceptible information, then data quantity is reduced, but compression efficiency is limited compared to AI-based methods
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with AI-based neural network systems. The neural networks learn optimal compression strategies through training, automatically identifying patterns and redundancies that rule-based systems cannot detect. This substitution enables superior compression efficiency while achieving the same or better data reduction ratios.
Solution Approach 2:
The patent transforms compression from a fixed algorithmic process to a learned adaptive process. By changing from deterministic compression rules to neural network-based adaptive compression, the system achieves higher compression efficiency. The neural networks adjust compression parameters dynamically based on content characteristics, optimizing the balance between data reduction and quality preservation.
Data Source
AI summary
A method for lossy video encoding, transmission and decoding, the method comprising the steps of: receiving an input video at a first computer system; encoding an input frame of the input video to produce a latent representation; producing a quantized latent; producing a hyper-latent representation; producing a quantized hyper-latent; entropy encoding the quantized latent; transmitting the entropy encoded quantized latent and the quantized hyper-latent to a second computer system; decoding the quantized hyper-latent to produce a set of context variables, wherein the set of context variables comprise a temporal context variable; entropy decoding the entropy encoded quantized latent using the set of context variables to obtain an output quantized latent; and decoding the output quantized latent to produce an output frame, wherein the output frame is an approximation of the input frame.