Adaptive Transform Function for Video Compression
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Solution Overview
Problem
Existing data compression algorithms often fail to effectively exploit redundancy in multimedia data sets due to restrictive assumptions about temporal correlation, leading to suboptimal compression performance.
Innovation Solution
The proposed solution involves calculating and encoding optimal residual data using multiple reference frames, sum of absolute differences (SAD) values, motion vector values, and block energy, with transform functions that differentiate between important and non-important information to achieve greater compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression algorithms use restrictive temporal relationship assumptions and limited reference frames, then device complexity is reduced and ease of operation is improved, but compression performance and redundancy exploitation are insufficient
Solution Approach 1:
The patent applies dynamics by making the reference frame selection adaptive rather than static. The algorithm dynamically determines which frames to use as references based on actual temporal correlation analysis, allowing the system to adapt to varying video content characteristics and achieve optimal compression performance without fixed restrictive assumptions
Solution Approach 2:
The patent changes key parameters including the number of reference frames (using multiple reference frames instead of limited ones), the temporal relationship assumptions (removing restrictive assumptions), and the residual signal generation method (using optimal residual signals based on actual correlation). These parameter changes enable better exploitation of redundancy while managing complexity through efficient algorithms
2Loss of information
If compression algorithms encode all residual information to maintain data quality, then information completeness is preserved, but data size and transmission requirements increase
Solution Approach 1:
The patent applies local quality by differentiating between important and non-important information within the residual signal. Instead of uniformly encoding all residual data, the algorithm selectively processes different regions and frequency components based on their importance to perceived quality, discarding or coarsely encoding less important information while preserving critical details
Solution Approach 2:
The patent implements discarding and recovering by selectively removing non-essential information from the residual signal during compression. The transform functions and encoding process are designed to discard information that can be reconstructed or is imperceptible, achieving compression while maintaining acceptable quality through intelligent selection of what to retain and what to discard
3Productivity
If transform functions process all residual values uniformly, then processing simplicity is maintained, but compression efficiency is reduced due to inability to differentiate important from non-important information
Solution Approach 1:
The transform functions are designed to process different residual values differently based on their characteristics and importance. The patent applies local quality by using adaptive transform strategies that select different processing methods for different regions, frequencies, or magnitudes of residual signals, enabling the system to differentiate between important and non-important information while maintaining manageable complexity through structured decision rules
Data Source
AI summary
Some representative embodiments are directed to systems and methods for compressing a data set. In one embodiment, a method comprises receiving a frame of data to be encoded, generating a residual frame that represents a difference between the received frame and one or several reference frames, performing a respective sum of absolute differences (SAD) calculation for each block within the residual frame, and applying a transform function to each data value within the residual frame, wherein the transform function is at least a function of a SAD value calculated for the block containing the respective data value.