Multichannel Haptic Encoding with Change-Based Channel Ordering
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Solution Overview
Problem
Current haptic data compression methods introduce delays that can significantly reduce user experience in haptic communication systems, particularly in teleoperation scenarios where low latency is crucial for maintaining transparency and task performance.
Innovation Solution
A multichannel haptic data compression system using a combination of source and sparse encoders/decoders that reorder and group codewords based on change detection, allowing only changed data to be transmitted, thereby reducing bitstream size and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to haptic data, then bitrate requirements are reduced, but delays are introduced that reduce user experience
Solution Approach 1:
The haptic data stream is segmented into multiple channels (e.g., position, velocity, acceleration channels) that are processed independently. This segmentation allows the system to identify and compress only the necessary information in each channel, reducing overall bitrate while maintaining real-time performance and minimizing delay through selective compression of critical data streams.
Solution Approach 2:
The patent extracts and transmits only the essential haptic parameters that are critical for user experience, removing redundant data. By identifying and retaining only the most important haptic information (such as significant changes in position or force), the system achieves bitrate reduction without introducing significant delays, as unnecessary data processing is eliminated.
2Quantity of substance
If complex compression algorithms are used, then compression ratio is improved, but computational complexity and processing time increase
Solution Approach 1:
The compression algorithm dynamically adapts its complexity based on the characteristics of the haptic data being processed. For static or slowly varying haptic signals, simpler compression methods are applied, while for dynamic signals requiring higher fidelity, more sophisticated algorithms are activated. This dynamic approach optimizes the balance between compression ratio and computational complexity in real-time.
Solution Approach 2:
The patent changes compression parameters such as quantization levels, prediction window sizes, and transformation types based on the specific haptic channel and data characteristics. By adjusting these parameters dynamically, the system achieves high compression ratios for suitable data types while maintaining low computational complexity for time-critical channels, resolving the trade-off between compression efficiency and processing burden.
Data Source
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AI summary
It is presented a method for encoding haptic data for a plurality of channels. The method comprises the steps of: determining an order of the plurality of channels in a first list based on a respective previous codeword data item for each of the plurality of channels, each previous codeword data item being either a no-change data item, or a change data item, wherein the order is such that all no-change previous codeword data items are arranged at one end of the first list; arranging current codewords in a second list accordance with the channel order of the first list; grouping the current codewords in consecutive groups; generating a group index indicating whether each group contains only no-change codewords or at least one change codeword; and outputting the group index along with current codewords, wherein current codewords are only output for groups containing at least one change codeword.