Adaptive Entropy Coding for Non-Stationary Video Symbol Frequencies

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Solution Overview

Problem

Existing video encoding and decoding technologies face challenges in maintaining accurate entropy coding probabilities, especially in non-stationary symbol frequency environments, which can lead to resource over-utilization and noise errors.

Innovation Solution

Adaptive stochastic entropy coding is employed, using backward, forward, or combined adaptation methods to continuously update probabilities, ensuring accurate encoding and decoding by synchronizing probabilities between the encoder and decoder, and reducing noise errors through modified probability distributions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If traditional entropy coding with fixed probability distributions is used, then device complexity is reduced, but manufacturing precision (encoding accuracy) deteriorates in non-stationary environments

Engineering Contradiction:

Improveencoding accuracyVSAvoidprobability update mechanism complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent implements dynamic probability distribution updates during the encoding process. The probability distribution is initially set based on training data, then adaptively updated using forward adaptation (incorporating encoded symbol frequencies) and backward adaptation (incorporating decoded symbol frequencies) to track non-stationary symbol frequencies in the input data stream, thereby maintaining encoding accuracy without requiring complete re-encoding

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs feedback mechanisms where the encoder monitors the actual symbol frequencies in the input data stream and compares them with the current probability distribution. Forward adaptation uses feedback from encoded symbols while backward adaptation uses feedback from decoded symbols, allowing the system to detect deviations and adjust probability distributions accordingly to maintain optimal encoding performance

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If frequent probability updates are performed to maintain accuracy, then manufacturing precision (encoding accuracy) is improved, but use of energy increases due to continuous processing

Engineering Contradiction:

Improveentropy coding accuracyVSAvoidprocessing energy consumption

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The patent performs probability distribution updates periodically at specific intervals during the encoding process rather than continuously for every symbol. Forward adaptation is performed after encoding blocks of symbols, and backward adaptation is performed after decoding blocks of symbols, reducing the computational frequency of updates while maintaining accuracy through periodic recalibration of the probability distribution

Inventive Principle:

Principle #19Periodic action

3Adaptability or versatility

If adaptive probability updating is implemented, then adaptability to non-stationary environments is improved, but device complexity increases due to additional processing steps

Engineering Contradiction:

Improveadaptability to non-stationary symbol frequenciesVSAvoidencoding process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent divides the input data stream into manageable blocks for processing. Probability distribution updates are performed on a block-by-block basis rather than symbol-by-symbol, segmenting the adaptive process into discrete, manageable units. This segmentation reduces the complexity of tracking and updating probabilities while maintaining adaptability to changing symbol frequencies within each block

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS10284854B2Adaptive stochastic entropy coding

Publication Date: 2019.05.07 GOOGLE LLC

AI summary

Adaptive stochastic entropy encoding may include identifying a current portion of an input video stream, and identifying a current probability distribution, which may be an adapted probability distribution associated with a previously encoded portion of the video stream. Adaptive stochastic entropy encoding may include identifying a forward update probability distribution based on the current portion, generating a modified probability distribution for the current portion based on the forward update probability distribution and the current probability distribution, generating an encoded portion based on the current portion and the modified probability distribution, and generating an adapted probability distribution based on the current probability distribution and the forward update probability distribution.