Entropy Encoding Cost Analysis for Video Compression
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Solution Overview
Problem
Current video compression techniques face challenges in efficiently transmitting high-resolution video over limited bandwidth channels, as they do not effectively optimize the encoding and decoding processes to minimize bandwidth consumption while maintaining video quality.
Innovation Solution
The method involves transforming a portion of the video signal into matrices of transform coefficients, quantizing them, and identifying an encoding context to determine the cost of entropy encoding non-zero values, with a processor setting transform coefficients within a specific range to zero to optimize encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video compression techniques are used, then video quality can be maintained, but bandwidth consumption increases
Solution Approach 1:
The patent dynamically changes the zero-bin width parameter based on the encoding context and cost analysis. By adjusting this parameter, the system optimizes the balance between video quality preservation and bandwidth consumption, allowing adaptive compression that responds to local image characteristics rather than using fixed compression parameters throughout the video stream
Solution Approach 2:
The system implements dynamic adaptation by continuously analyzing the encoding context and adjusting the zero-bin width accordingly. This dynamic approach allows the compression algorithm to optimize bandwidth usage in real-time while maintaining video quality where necessary, creating a flexible system that adapts to varying content characteristics
2Measurement precision
If more transform coefficients are preserved, then video quality improves, but encoding complexity increases
Solution Approach 1:
The patent applies different zero-bin widths to different regions of the transform coefficient matrix based on local encoding context. This local quality approach allows the system to preserve important coefficients in critical regions while applying more aggressive compression in less important regions, optimizing the balance between quality and complexity on a localized basis
Solution Approach 2:
The system performs preliminary analysis of the encoding context before finalizing the encoding decisions. By pre-calculating the cost of entropy encoding and determining appropriate zero-bin widths in advance, the system reduces the computational complexity of the actual encoding process while maintaining optimal quality-compression balance
Data Source
AI summary
Systems, methods, and apparatuses for compressing a video signal are disclosed. In one embodiment the method includes transforming at least a portion of the video signal to produce matrices of transform coefficients, dividing the transform coefficients by at least one quantizer value to generate matrices of quantized transform coefficients, and identifying an encoding context including the transformed portion of the video signal. The method may further include determining a cost to entropy encode a non-zero value occurring within the encoding context, determining a first range including zero and having a width that is a function of the determined cost to entropy encode a non-zero value, and setting to zero at least one transform coefficient that falls within the first range.