Image Frame Freeze Detection Using Early-Stage CRC Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image frame freeze detection methods in vehicle imaging systems are inefficient, often leading to increased latency and false negatives due to hardware accelerators being too far removed from the image data source and processors requiring intensive computational resources for pixel-by-pixel comparisons.
Innovation Solution
Implementing a hardware accelerator that compares cyclic redundancy check (CRC) signatures of successive image frames at an early stage in the data processing pipeline, reducing latency and false negatives by analyzing transformed image data statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a hardware accelerator is used to detect frame freezes, then detection speed is improved, but the accelerator being too far removed from the image data source increases latency
Solution Approach 1:
The patent applies preliminary action by performing CRC signature calculation on image data at an early stage in the processing pipeline, before the data reaches later processing stages. The hardware accelerator is positioned to receive image data directly from the memory interface early in the pipeline, calculate CRC signatures, and compare them with previous frame signatures to detect freezes. This early intervention reduces latency by avoiding the need to wait for data to propagate through multiple processing stages before detection can occur.
2Measurement precision
If pixel-by-pixel comparison is used for frame freeze detection, then detection accuracy is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent extracts the essential characteristic of image frames by calculating CRC (cyclic redundancy check) signatures, which provide a compact numerical representation of the entire frame content. Instead of comparing all pixel values between frames, the system computes CRC signatures for each frame and compares these condensed representations. This extraction approach maintains detection accuracy while dramatically reducing computational complexity and processing requirements, as CRC calculation is much more efficient than pixel-by-pixel comparison.
3Loss of time
If early stage CRC comparison is implemented, then detection latency is reduced, but hardware complexity increases
Solution Approach 1:
The patent introduces CRC signature calculation as an intermediary step between raw image data acquisition and frame freeze detection. The hardware accelerator includes a CRC calculation unit that processes image data and generates signature values, which are then stored and compared with previous frame signatures. This intermediary mechanism enables efficient freeze detection through simple signature comparison rather than complex pixel analysis, reducing overall system latency while the modular hardware design keeps complexity manageable through specialized functional blocks.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods, apparatus, systems, and articles of manufacture are described for image frame freeze detection. An example hardware accelerator (204) includes a core logic circuit (208) to generate second image data based on first image data associated with a first image frame, the second image data corresponding to at least one of processed image data, transformed image data, or one or more image data statistics, a load/store engine (LSE) (206) coupled to the core logic circuit, the LSE to determine a first CRC value based on the second image data obtained from the core logic circuit, and a first interface (230) coupled to a second interface (224), the second interface coupled to memory (212), the first interface to transmit the first CRC value obtained from the memory to a host device (214 A; 214B).