Block-Based Sample-Rate Converter for Image Processing
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Solution Overview
Problem
Conventional image and video scaling techniques consume significant computational resources and require excessive memory and bandwidth, making them challenging for real-time applications, especially when dealing with varying image resolutions and two-dimensional sample-rate conversion.
Innovation Solution
The proposed technique leverages inherent similarities and symmetries in image data to limit filter switching and process data efficiently, using pre-processing to correlate input samples with filters for each output pixel, and integrating this method within a decoding pipeline to minimize memory transfers and optimize computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional poly-phase filters are used for sample-rate conversion on a sample-by-sample basis, then image scaling accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent pre-calculates and stores filter coefficients for all possible phase values before actual image scaling. During scaling operations, the pre-computed filters are directly applied without real-time calculation, significantly reducing computational load while maintaining scaling accuracy
Solution Approach 2:
The patent divides the continuous filter selection process into discrete segments based on phase values. By quantizing the phase into a finite set of values and pre-computing corresponding filters for each phase segment, the system transforms an infinite computational problem into a manageable discrete set of pre-computed solutions
2Adaptability or versatility
If image data is processed through multiple memory transfers in conventional scaling pipelines, then data access flexibility is improved, but bandwidth requirements and processing time increase
Solution Approach 1:
The patent combines multiple memory access operations into a single unified memory transfer. By reorganizing the scaling pipeline to process entire blocks of image data in one pass with direct memory-to-memory copying, the system eliminates intermediate storage and multiple transfer stages, reducing bandwidth requirements and improving processing speed
3Manufacturing precision
If dynamic filter selection is implemented for each output sample based on spatial relationship, then scaling precision is improved, but device complexity and computation time increase
Solution Approach 1:
The patent pre-computes and stores filter coefficients for all possible phase relationships between input and output samples before actual scaling operations. During scaling, the system only needs to look up pre-computed filters based on the current phase value, transforming complex real-time calculations into simple table lookups that maintain precision while dramatically reducing computation time
Data Source
AI summary
A new technique for image sample-rate conversion can be efficiently implemented with respect to memory, bandwidth and computational requirements. According to one aspect, the technique takes advantage of certain inherent similarities and symmetries within the image data to limit switching of filters during processing and to otherwise process the data in an efficient order. According to another aspect, the technique can be implemented within a decoding pipeline such as a JPEG/MPEG decoding pipeline in such a way that multiple transfers of image data in and out of the external memory can be avoided. According to a still further aspect, where poly-phase filters are used, computationally-efficient filters are chosen for use with the image conversion process. The technique is amenable to both hardware and software based implementations.