Block-Based Video Dropout Detection Using Activity Block Analysis
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Solution Overview
Problem
Existing methods for detecting block-based video dropouts in compressed digital video are either inaccurate or excessively computation-intensive, leading to inefficiencies in video transmission and decoding.
Innovation Solution
A system and method that identifies activity blocks, detects candidate error blocks using horizontal and vertical lines, and validates their presence across fields to efficiently detect dropout error blocks, employing modules for activity block identification, horizontal and vertical lines detection, memory storage, comparison, and start/end validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing algorithms are used to detect video dropout error blocks, then detection accuracy may be achieved, but computational complexity becomes extremely high
Solution Approach 1:
The patent divides the video frame into multiple blocks and processes each block independently to detect error blocks. By segmenting the detection task into smaller block-level operations rather than processing the entire frame globally, the computational complexity is reduced while maintaining detection accuracy through localized analysis of each block's characteristics
Solution Approach 2:
The patent applies different detection strategies and thresholds to different blocks based on their local characteristics. Activity blocks with high variance undergo different processing compared to low-activity blocks, allowing the system to maintain high detection accuracy for error blocks while reducing computational effort in regions less likely to contain errors
2Measurement precision
If comprehensive detection methods are applied to all video blocks, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary classification of blocks into activity blocks and non-activity blocks based on simple variance calculations before applying more complex detection methods. This preliminary action allows the system to quickly identify and focus computational resources only on blocks that are likely to contain errors, reducing overall processing time while maintaining detection accuracy
Solution Approach 2:
The patent applies full detection algorithms only to activity blocks that meet certain criteria, while using simplified or no detection on non-activity blocks. This partial application of detection methods reduces processing time by avoiding unnecessary computations on blocks unlikely to contain errors, while still maintaining high detection accuracy for the blocks that matter most
Data Source
AI summary
Methods and systems for detecting block based video dropouts in one or more fields associated with various video frames is provided. A current field is divided into a plurality of blocks. A set of activity blocks is identified from the plurality of blocks. The activity blocks are then processed to identify horizontal and vertical lines which are then further processed to form one or more candidate error blocks. The candidate error blocks are validated for start and end to determine a count of video dropout errors associated with the current field.