Video Frame Blockiness Detection Using Pixel Difference Analysis
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Solution Overview
Problem
Existing solutions to reduce video quality artifacts such as blockiness, macroblocking, and pixelation in encoded frames are computationally cumbersome and suboptimal, primarily focusing on the spatial domain.
Innovation Solution
A method to detect blockiness by analyzing cumulative differences between neighboring pixel values within a frame on a row-by-row and column-by-column basis, using data structures to quantify blockiness, and implementing remedial actions when the blockiness exceeds an acceptable level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing spatial domain solutions are used to reduce blockiness, then video quality artifacts are addressed, but computational complexity increases and effectiveness is suboptimal
Solution Approach 1:
The patent transitions from spatial domain analysis to frequency domain analysis by applying Fast Fourier Transform (FFT) to convert spatial blockiness patterns into frequency components. This dimensional transformation enables more efficient detection and processing of blockiness artifacts, resolving the contradiction between video quality improvement and computational complexity by leveraging mathematical transforms that simplify the analysis space.
Solution Approach 2:
The patent changes the parameter space from spatial pixel values to frequency domain coefficients through FFT transformation. By analyzing blockiness in the frequency domain rather than the spatial domain, the system achieves more effective artifact detection with reduced computational burden, as frequency domain operations are more efficient for pattern recognition and quantification.
2Ease of manufacture
If spatial domain analysis is used for blockiness detection, then implementation is straightforward, but detection accuracy and effectiveness are suboptimal
Solution Approach 1:
The patent applies Fast Fourier Transform to convert the image from spatial domain to frequency domain, enabling more accurate blockiness detection. The frequency domain representation reveals periodic patterns and artifacts more clearly than spatial domain analysis, significantly improving detection accuracy while maintaining implementation feasibility through standard signal processing algorithms.
Data Source
AI summary
Methods, systems, and apparatuses for detecting blockiness in frames of content are described herein. Frame of content may include blocks of pixels. Blockiness may be perceptible in encoded frames, once decoded, due to encoding-related artifacts and/or other artifacts related to processing and/or delivery of the content. Cumulative differences between neighboring pixel values within a frame on a row-by-row and/or a column-by-column basis may be represented in data, such as a data structure. The data may indicate an amount of blockiness within the frame. When the amount of blockiness is beyond an acceptable level, one or more remedial actions may be performed to lessen an amount of blockiness in other frames of the content.