Video Frame GoP Structure Estimation via Binary Pattern Matching

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Solution Overview

Problem

Existing methods for estimating the Group of Picture (GoP) structure in video streams are unreliable due to packet loss and encryption, which complicates the discrimination between Intra-frames (I-frames), Predicted-frames (P-frames), and Bidirectional-frames (B-frames, especially in H.264 encoders with hierarchical coding.

Innovation Solution

A method that captures frame sizes in a video stream, converts them into binary patterns, and matches these patterns against predefined patterns to estimate the GoP structure, allowing for frame-type classification independent of size assumptions, even in encrypted or packet-loss-affected streams.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If frame type estimation is performed in encrypted or packet-loss-affected video streams, then the ability to monitor video quality is maintained, but the reliability of frame type discrimination deteriorates

Engineering Contradiction:

Improvevideo quality monitoring capabilityVSAvoidframe type discrimination accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary actions by capturing frame sizes and converting them to binary patterns before pattern matching occurs. This preprocessing enables the system to work with limited information from encrypted or packet-loss-affected streams, establishing a foundation for reliable GoP structure estimation even when direct frame type information is unavailable

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a copy of the frame size information in binary form (0s and 1s) that represents the GoP structure. This binary pattern copy can be matched against predefined patterns without requiring access to the original encrypted bitstream or complete frame data, thus maintaining monitoring capability while working around encryption and packet loss

Inventive Principle:

Principle #26Copying

2Device complexity

If traditional frame type estimation methods are used, then the processing is simpler, but the accuracy deteriorates due to size assumptions and packet loss

Engineering Contradiction:

Improveprocessing simplicityVSAvoidframe type estimation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the parameter representation from raw frame sizes to binary patterns (0s and 1s) that encode GoP structure information. This parameter transformation allows the system to extract structural information without relying on absolute size values, making the estimation robust against packet loss and encryption while maintaining processing feasibility

Inventive Principle:

Principle #35Parameter changes

3Reliability

If packet loss protection mechanisms are implemented, then video quality is improved, but the system complexity increases

Engineering Contradiction:

Improvevideo qualityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical or protocol-level packet loss protection mechanisms with a simpler information-theoretic approach using binary pattern matching. Instead of implementing retransmission or error correction systems, the method uses statistical patterns in frame sizes to infer GoP structure, achieving robustness against packet loss through algorithmic simplicity rather than system complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS9241156B2Method for estimating the type of the group of picture structure of a plurality of video frames in a video stream

Publication Date: 2016.01.19 DEUTSCHE TELEKOM AG

AI summary

A method for estimating the type of the GoP structure of a plurality of video frames in a video stream by estimating their frame types includes: capturing frame sizes in bytes of every video frame subsequent to an initial I-frame to obtain an array of frame sizes by exploiting features of a transport layer carrying the video stream; converting, after a number of frames, the array of frame sizes into an array of zeros and ones; matching the binarized array of frame sizes to a number of predefined short basic binary patterns, said predefined binary patterns depicting all GoP structures to be considered; converting the result of said matching to form a single score value; and determining the particular pattern of the number of predefined patterns of binaries having the best score value, according to a predefined metric.