Multi-Level Encoding for Point Cloud Image Compression
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Solution Overview
Problem
Existing image encoding technologies face challenges in compressing high bit-depth images without loss, particularly in point cloud applications, where lossy compression methods result in sub-par performance and quality degradation.
Innovation Solution
A system and method that encodes N-bit pixel values into M-bit image frames by rounding pixel values and determining error values, allowing for lossless or near-lossless compression by using multiple sub-image frames and applying spatial smoothing filters to improve compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression algorithms are used to convert high bit-depth images to lower bit-depth images, then compression efficiency is improved, but image quality deteriorates
Solution Approach 1:
The patent segments the compression process into multiple passes: a first pass that performs lossy compression to achieve high compression efficiency, and a second pass that applies lossless compression to the residual data. This segmentation allows the system to achieve both high compression efficiency and high image quality by addressing different aspects of the data in separate stages.
Solution Approach 2:
The patent introduces an intermediary residual data structure that captures the difference between the original high bit-depth image and the compressed low bit-depth representation. This residual data serves as a mediator that preserves the lost information, allowing it to be recovered in a subsequent lossless compression pass without degrading image quality.
2Manufacturing precision
If multiple sub-image frames are used to encode error values, then compression quality is improved, but device complexity increases
Solution Approach 1:
The patent segments the image data into multiple components: the main compressed image data and separate residual error data. By organizing the encoding into distinct passes and data structures, the complexity is managed through clear separation of concerns, where each pass handles a specific aspect of compression without requiring the entire system to be redesigned.
Solution Approach 2:
The patent applies lossless compression selectively to the residual error data rather than the entire image stream. This partial application of lossless techniques only where needed (in the residual data) achieves high compression quality while avoiding the excessive complexity that would result from applying lossless compression to all image data.
3Productivity
If spatial smoothing filters are applied to up-scaled pixel values, then compression performance is improved, but processing time increases
Solution Approach 1:
The patent applies spatial smoothing filters as a preliminary step before the main compression process. By pre-processing the image data to reduce high-frequency variations and enhance spatial coherence, the subsequent compression algorithms can achieve better performance with fewer operations, thereby reducing overall processing time despite the added filtering step.
Data Source
AI summary
A system comprises an encoder configured to compress images, such as image frames comprising attribute information and/or spatial for a point cloud and/or an occupancy map for the point cloud. Also, a system includes a decoder configured to decompress compressed image frames, such as image frames comprising compressed attribute and/or spatial information for the point cloud or an occupancy map for the point cloud. Additionally, the encoder may map N-bit data to M-bit code words, where M is less than N. Alternatively the encoder may map N-bit data to M-bit code words, where M is greater than N. In a similar manner, a decoder may map the M-bit code words back to the N-bit data.