Context-Based V-Mesh Displacement Encoding for 3D Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently compressing and transmitting 3D point clouds and meshes due to their large data requirements, necessitating specialized hardware and inefficient bandwidth usage.
Innovation Solution
The use of context-based encoding and decoding methods for displacement fields in V-MESH, which form and manipulate level of detail (LOD) signals to produce optimized bitstreams for efficient compression and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used for 3D point clouds and meshes, then the data can be stored and transmitted, but the bandwidth requirement becomes excessively large and specialized hardware is needed
Solution Approach 1:
The patent segments the 3D mesh data into multiple levels of detail (LOD) signals through wavelet transform, allowing different parts of the data to be processed and transmitted at different precision levels. This segmentation enables efficient compression without requiring specialized hardware for the entire dataset.
Solution Approach 2:
The patent changes the representation parameters of the mesh data by transforming it into frequency domain coefficients through wavelet transform. This parameter transformation allows standard compression techniques to be applied, eliminating the need for specialized hardware while reducing bandwidth requirements.
2Measurement precision
If high precision 3D content is transmitted, then the quality and immersion experience are improved, but the bitrate increases significantly
Solution Approach 1:
The patent applies partial action by transmitting only the necessary levels of detail based on viewer position and interest. The wavelet transform allows selective transmission of significant frequency components, providing high precision where needed while reducing overall bitrate by omitting less important details.
Solution Approach 2:
The patent transitions from spatial domain representation to frequency domain representation through wavelet transform. This dimensional change allows for more efficient data compression while maintaining quality, as frequency domain coefficients can be more effectively thresholded and compressed compared to direct spatial data.
3Productivity
If displacement fields are encoded without context, then the encoding process is simple, but the compression efficiency is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the encoder uses previously encoded displacement field data to generate context models that improve subsequent encoding decisions. This feedback loop allows the system to adapt to local patterns in the displacement field, significantly improving compression efficiency through context-aware encoding.
Data Source
AI summary
An apparatus includes a communication interface and a processor operably coupled to the communication interface. The processor is configured to form a level of detail (LOD) signal corresponding to a displacement field. The processor is also configured to identify a current sample in the LOD signal. The processor is further configured to derive a context for the current sample in the LOD signal. In addition, the processor is configured to produce an output bitstream by encoding the LOD signal using the context.