Small Image Tile Compression for Lower Hardware and Latency
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Solution Overview
Problem
Existing lossy compression systems for images compress the entire image/frame as a whole, which can involve high hardware and latency costs.
Innovation Solution
Implementing lossy compression technology specifically for small image tiles, allowing for more efficient processing and reduced hardware and latency requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire image/frame compression is implemented, then compression quality is maintained, but hardware requirements and latency increase
Solution Approach 1:
The patent divides the image into multiple tiles and processes each tile independently through separate compression pipelines. This segmentation allows parallel processing of multiple tiles simultaneously, reducing the hardware complexity required for any single pipeline while maintaining overall compression quality across the entire image.
Solution Approach 2:
The patent applies lossy compression selectively to small image tiles rather than the entire image, using a compression approach that is appropriate for the specific size and characteristics of each tile. This partial action on subsets of the image allows optimization of compression parameters for tile-specific requirements.
2Reliability
If entire image/frame compression is implemented, then comprehensive compression is achieved, but processing latency increases
Solution Approach 1:
By segmenting the image into multiple small tiles that can be compressed in parallel, the overall processing time is reduced while maintaining comprehensive compression coverage. Each tile is processed independently and simultaneously, eliminating the sequential processing bottleneck of traditional whole-image compression.
Solution Approach 2:
The patent enables continuous compression processing by maintaining multiple active compression pipelines that work simultaneously on different tiles. This continuous parallel action eliminates idle time between processing stages and maintains high utilization of compression resources throughout the process.
3Productivity
If small image tile compression is implemented, then hardware efficiency is improved, but image processing complexity increases
Solution Approach 1:
The patent manages processing complexity by segmenting the image into standardized small tiles with uniform dimensions and processing requirements. This standardization allows reuse of the same compression pipeline configuration across all tiles, reducing the overall system complexity despite the increased number of processing units required.
Solution Approach 2:
The patent creates a universal compression pipeline that can process any small image tile of standardized dimensions. This multi-functional pipeline design reduces complexity by using the same hardware block for all tile processing rather than requiring specialized processors for different image regions or sizes.
Data Source
AI summary
Methods, systems and apparatuses provide for encoder technology that conducts a spatial transformation on tiles in a block of an image, wherein the spatial transformation is conducted on a per tile basis and results in a first sub-band data and second sub-band data, predicts residual data from the first sub-band data, and generates quantization data from the second sub-band data, wherein the residual data and the quantization data represent a lossy compressed portion of the image. Additionally, decoder technology may recover first sub-band data from residual data, scale up to second sub-band data from quantization data, wherein the residual data and the quantization data represent a lossy compressed portion of an image, and conduct an inverse spatial transformation on the first sub-band data and the second sub-band data, wherein the inverse spatial transformation is conducted on a per tile basis and results in tiles in a block of the image.