Video Compression Transformer for Bias-Free Temporal Entropy Coding
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Solution Overview
Problem
Existing video compression techniques rely on architectural biases and priors, resulting in complex models that are difficult to implement and perform well only on data matching these biases, limiting their effectiveness.
Innovation Solution
Replace motion prediction and warping operations with a transformer-based temporal entropy model that learns dependencies between video frames, allowing for efficient compression without architectural biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion prediction and warping operations are used, then video compression performance is improved, but model complexity increases and implementation becomes challenging
Solution Approach 1:
The patent extracts and removes the complex motion prediction and warping operations from the video compression model. Instead of using traditional motion compensation techniques, the invention uses a transformer-based approach that directly models temporal dependencies between video frames, eliminating the need for separate motion estimation and warping steps while achieving comparable or superior compression performance
Solution Approach 2:
The patent replaces the mechanical-like traditional video compression operations (motion prediction, warping, residual compensation) with a neural transformer model. The transformer uses self-attention mechanisms to capture temporal dependencies directly from video frames, substituting the step-by-step mechanical processing with a learned neural representation that achieves the same compression goals without the associated complexity
2Ease of manufacture
If traditional video compression techniques are used, then implementation is straightforward, but performance is limited to data matching architectural biases
Solution Approach 1:
The transformer-based model provides universality by learning general temporal dependency patterns from diverse video data during training. The model can handle various video operations and scenarios (motion, texture, lighting changes, etc.) without requiring separate specialized components for each, making it adaptable to unseen video content while maintaining implementation simplicity through a unified architecture
3Reliability
If complex connections between sub-components are used, then rate-distortion performance improves, but the system becomes complicated and difficult to implement
Solution Approach 1:
The patent merges multiple traditional video compression sub-components (motion prediction, warping, residual compensation, entropy modeling) into a single transformer-based temporal entropy model. This unified approach captures the essential temporal dependencies in video data through self-attention mechanisms, achieving high rate-distortion performance while simplifying the overall system architecture and eliminating the need for complex inter-component connections
Data Source
AI summary
Apparatus and methods related to encoding, by an encoder of a transmitting computing device, a plurality of successive input video frames as a corresponding sequence of quantized representations; predicting, by a transformer of the transmitting computing device, a probability mass function (PMF) as a conditional distribution of a given quantized representation in the sequence of quantized representations, wherein the conditional distribution is based on at least one dependency between one or more quantized representations that occur prior to the given quantized representation in the sequence of quantized representations; generating, by the transmitting computing device, a plurality of compressed video frames by applying, based on the predicted PMF, an entropy coding to each quantized representation, wherein the entropy coding comprises assigning a smaller number of bits to values that have a higher frequency of occurrence; and transmitting, by the transmitting computing device, the plurality of compressed video frames.