Convolution Padding Selection for Artifact-Aware Data Encoding
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently encoding and decoding multi-dimensional data sets, particularly in retaining data size and minimizing perceptible artifacts while accommodating diverse applications ranging from human consumption to machine processing.
Innovation Solution
The implementation of padding functions and discrete convolutions to manipulate and process multi-dimensional data sets, followed by cropping to achieve desired dimensions, along with entropy encoding and decoding techniques to generate and reconstruct data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If padding is performed to maintain data set size for convolution operations, then data size is retained, but additional computational overhead and complexity are introduced
Solution Approach 1:
The patent applies padding to the input data set before performing convolution operations. This preliminary action ensures that the data set has the appropriate size and shape required for the convolution process, allowing the operation to proceed without errors while maintaining the intended output dimensions.
Solution Approach 2:
Padding acts as an intermediary element added to the input data set. This intermediary layer of values (typically zeros or replicated boundary values) facilitates the convolution operation by providing the necessary spatial extent, allowing the filter to operate correctly without modifying the core data structure.
2Productivity
If cropping is performed to achieve desired dimensions after convolution, then output size is controlled, but data loss and potential artifact introduction occur
Solution Approach 1:
The patent performs cropping after the convolution operation to achieve the desired output dimensions. This preliminary sizing ensures that the output data set matches the target specifications, allowing for efficient downstream processing while controlling the final data structure.
Solution Approach 2:
The cropping operation extracts the desired portion of the convolved data set, removing excess data that extends beyond the target dimensions. This extraction process achieves the required output size while separating the useful data from the unnecessary portions.
3Adaptability or versatility
If multiple padding functions are supported for different applications, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements multiple padding functions that can be dynamically selected based on the specific application requirements. The system can adaptively choose from different padding strategies (such as zero-padding, replication-padding, or reflection-padding) depending on the data characteristics and desired outcome, making the system flexible without being rigidly fixed to a single approach.
Solution Approach 2:
The patent creates a universal padding mechanism that serves multiple functions across different applications. The same padding infrastructure supports various padding types and configurations, allowing a single system to handle diverse data sets and requirements without requiring separate specialized components for each case.
Data Source
AI summary
A method of encoding data includes: receiving an input data set having an arbitrary size about a height dimension and a width dimension; padding the input data set according to a padding function selected from a set of padding functions, such that a data set having a desired size about the height dimension and the width dimension is obtained; generating an output data set by performing a discrete convolution on the obtained data set; and generating a signal providing information corresponding to the output data set and the selected padding function.