Vehicle Lighting Beam Projection Using Fast Dictionary Decompression
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Solution Overview
Problem
Existing motor-vehicle lighting systems face challenges in projecting dynamic light beams due to slow decompression speeds of compressed images, which affect the visual quality and efficiency of adaptive lighting functionalities.
Innovation Solution
A method using dictionary-based decompression algorithms that leverage the similarity between consecutive images to enhance compression and decompression speeds, allowing for faster projection of dynamic lighting beams by adding decompressed data sequences from the current or previous images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compressed images are used to reduce data transmission bandwidth, then bandwidth consumption is reduced, but decompression speed becomes a limiting factor for projection speed
Solution Approach 1:
The image data is segmented into multiple data sequences that can be independently decompressed and processed. This allows parallel processing of different parts of the image, increasing overall decompression speed while maintaining the compression benefits for bandwidth reduction.
Solution Approach 2:
The patent performs preliminary decompression of data sequences before the actual projection is needed. By pre-processing and preparing the decompressed image data in advance, the system reduces the critical path delay during real-time projection, thus improving projection speed without sacrificing compression efficiency.
2Quantity of substance
If dictionary-based compression algorithms are used to improve compression rate, then storage efficiency is improved, but decompression complexity increases
Solution Approach 1:
The decompression process is divided into multiple independent stages corresponding to different data sequences. Each sequence can be decompressed using simpler operations, and the results are combined to form the complete image. This segmentation reduces the complexity of any single decompression operation while maintaining overall compression efficiency.
Solution Approach 2:
The patent applies partial decompression where only the necessary portions of the compressed data are fully decompressed based on the specific projection requirements. This selective approach reduces unnecessary computational complexity while maintaining the high compression rates benefits of dictionary-based algorithms.
3Manufacturing precision
If more data sequences are decompressed from previous images to improve visual quality, then image quality is improved, but memory usage increases
Solution Approach 1:
The patent applies different decompression strategies to different regions or sequences of the image based on their importance and similarity to previous frames. Critical regions that benefit most from temporal compression are decompressed with higher fidelity, while less critical regions use more aggressive compression, thus improving overall visual quality without proportionally increasing memory usage.
Solution Approach 2:
The system selectively retains and discards data sequences from previous images based on their utility for current projection needs. When memory is constrained, less important historical data is discarded or compressed more aggressively, while critical data is retained in higher quality. This dynamic management improves visual quality where needed while controlling overall memory consumption.
Data Source
AI summary
The invention relates to a method for projecting a dynamic lighting beam using a lighting system of a motor vehicle based on a compressed video. The method includes reading each image from the compressed video, decompressing the image for each of the read images using a dictionary-based decompression algorithm, each data sequence being decompressed either in a first mode in which the decompressed image has added to the image a copy of the sequence, or in a second mode including adding a data sequence of the previously read image added to the decompressed image, or in a third mode including adding a data sequence of a previously decompressed image. The method also includes projecting a light beam based on each decompressed image.