Video Compression Coefficient Reordering and Bitstream Partitioning
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Solution Overview
Problem
Existing video compression technologies face inefficiencies in bandwidth usage, computational load distribution, and error recovery, particularly in high-data-rate environments and video conferencing over restricted bandwidth links, due to fixed scan orders, bottlenecks in entropy coding, and limitations in motion prediction and error correction.
Innovation Solution
The method involves dynamically reordering discrete cosine transform coefficients based on their likelihood of being non-zero, partitioning the bitstream for optimized entropy coding, selecting interpolation methods for fractional pixel motion prediction, and using an alternate reference frame for efficient error recovery, allowing for adaptive compression and improved resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed scan orders are used for coefficient transmission, then device complexity is reduced, but video compression efficiency deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from fixed scan orders to dynamic coefficient reordering. The system adapts the transmission order of discrete cosine transform coefficients based on their statistical characteristics and importance, allowing the coding structure to change dynamically rather than following a rigid predetermined pattern. This resolves the contradiction by making the system flexible enough to optimize compression efficiency while maintaining manageable complexity through adaptive rather than static rules.
Solution Approach 2:
The patent changes parameters by modifying the transmission order of coefficients based on their statistical properties. Instead of using a fixed scan pattern, the system adjusts the ordering parameters dynamically according to the actual data characteristics, such as the distribution of non-zero coefficients and their magnitudes. This parameter adaptation enables better compression efficiency without requiring fundamentally complex restructuring of the coding framework.
2Device complexity
If centralized entropy coding is used, then device complexity is reduced, but productivity deteriorates due to bottlenecks
Solution Approach 1:
The patent applies segmentation by dividing the bitstream into multiple independent partitions, each of which can be entropy coded separately. This parallelization strategy breaks the centralized coding bottleneck by allowing multiple coding operations to occur simultaneously across different partitions. The segmentation maintains relatively simple device structure while dramatically improving processing throughput by eliminating the single-point bottleneck of centralized entropy coding.
3Device complexity
If conventional motion prediction is used, then device complexity is reduced, but loss of information increases in high-data-rate environments
Solution Approach 1:
The patent applies dynamics by implementing adaptive motion prediction that adjusts its behavior based on the data rate environment. In high-data-rate environments, the system dynamically switches to more sophisticated prediction methods that reduce information loss, while in bandwidth-constrained scenarios it falls back to simpler approaches. This dynamic adaptation resolves the contradiction by allowing the system to optimize prediction accuracy when the bandwidth permits without requiring permanently complex structures.
4Device complexity
If standard error recovery using immediate previous frame is used, then device complexity is reduced, but reliability deteriorates in packet-loss environments
Solution Approach 1:
The patent applies preliminary action by preparing multiple candidate reference frames in advance before packet loss occurs. Instead of relying solely on the immediately previous frame, the system pre-positions several potential reference frames that can be quickly substituted if transmission errors occur. This preliminary preparation enhances reliability in packet-loss environments without requiring complex real-time error recovery mechanisms, as the appropriate reference frame is already ready for immediate use.
Data Source
AI summary
A method of compressing video data having at least one frame having at least one block and each block having an array of pixels is provided. The method transforms the pixels of each block into coefficients and creates an optimal transmission order of the coefficients. The method also optimizes the speed of processing compressed video data by partitioning the data bitstream and coding each partition independently. The method also predicts fractional pixel motion by selecting an interpolation method for each given plurality or block of pixels depending upon at least one metric related to each given block and varies the method from block to block. The method also enhances error recovery for a current frame using a frame prior to the frame immediately before the current frame as the only reference frame for lessening quality loss during data transmission. Enhanced motion vector coding is also provided.