Depthmap Compression via Normalization and Logarithmic Scaling
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Solution Overview
Problem
Depthmaps are large files that consume significant bandwidth when served across a network and often require proprietary decoders for retrieval, posing challenges in client-server environments.
Innovation Solution
A system and method for compressing depthmaps by normalizing them based on minimum and maximum values using encoding models, followed by compression using JPEG or lossless image compression schemes, and packaging metadata for efficient transmission and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If depthmaps are transmitted across a network, then depth information can be shared between client-server, but bandwidth consumption increases significantly
Solution Approach 1:
The patent applies parameter changes by normalizing depth values to a standard range (0-1) and converting them to logarithmic scale. This transforms the depth data into a compressed representation that maintains relative depth relationships while significantly reducing the numerical range and perceived data size, thereby lowering bandwidth consumption during transmission.
Solution Approach 2:
The patent segments the depthmap processing into distinct stages: normalization stage where depth values are transformed to a standard range, compression stage where the normalized data is encoded using efficient algorithms, and transmission stage where the compressed data is sent across the network. This segmentation allows each stage to optimize for its specific function, reducing overall bandwidth usage.
2Reliability
If proprietary decoding methods are used for depthmaps, then depth data can be retrieved, but client device complexity increases
Solution Approach 1:
The patent implements universality by encoding depthmaps using standard, widely-supported compression formats (such as JPEG 2000 or PNG with lossless compression) that can be decoded by generic image viewing software and standard libraries. This eliminates the need for proprietary decoders, allowing any client device with basic image processing capability to retrieve and display the depth data without requiring specialized software or hardware.
3Loss of energy
If depthmaps are compressed using standard compression, then bandwidth is reduced, but depth precision may be lost
Solution Approach 1:
The patent applies preliminary action by performing normalization and logarithmic transformation on the depth data before compression. This pre-processing step transforms the depth values into a compressed representation that maintains relative depth relationships and precision characteristics while reducing the dynamic range. The normalization preserves the essential depth information structure, ensuring that when the data is decompressed, the original depth relationships are maintained with minimal loss.
Data Source
AI summary
Systems and methods for compressing a depthmap are provided. In some aspects, a system includes an encoding module configured to determine a minimum value and a maximum value of a depthmap. The encoding module is further configured to normalize the depthmap based on the minimum value, the maximum value, and an encoding model. The normalized depthmap includes a scalar value for each pixel of the depthmap. The system also includes a compression module configured to compress the normalized depthmap.