Hierarchical Signal Decoding for Bandwidth-Efficient Object Analysis
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Solution Overview
Problem
Existing image recognition systems face inefficiencies in processing uncompressed video signals due to bandwidth bottlenecks and the need for uniform resolution and color space across multiple classification tasks, leading to costly pre-processing operations and prolonged processing times.
Innovation Solution
A method and system that utilize hierarchical compression to decode input signals at varying levels of quality and resolution, allowing selective decoding of regions of interest and color planes, enabling efficient classification tasks using compressed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uncompressed video signals are distributed to multiple nodes in a cluster for classification tasks, then all classification services can access the full quality signal, but network bandwidth and memory bandwidth bottlenecks occur
Solution Approach 1:
The video signal is segmented into multiple quality levels (e.g., full resolution, half resolution, quarter resolution). Different classification services receive different segments based on their requirements. This allows the system to provide appropriate quality to each service without distributing the entire high-bandwidth uncompressed signal to all nodes, thereby reducing overall bandwidth consumption while maintaining service reliability.
Solution Approach 2:
Different regions or aspects of the video signal are provided at different quality levels to different services. For example, services that require fine temporal granularity receive higher quality segments, while services that can tolerate lower quality (such as those analyzing multiple frames) receive lower quality segments. This local differentiation optimizes bandwidth usage while ensuring each service receives adequate signal quality for its specific task.
2Productivity
If independent classification services are run on a cluster with suitable computing resources, then multiple classification tasks can be performed simultaneously, but pre-processing operations become costly and processing time increases
Solution Approach 1:
The video signal is pre-segmented into different quality levels before being distributed to the cluster nodes. This preliminary segmentation avoids the need for each node to perform costly pre-processing operations on the full-resolution signal. Each node receives pre-prepared segments at the appropriate quality level for its classification task, significantly reducing pre-processing time while maintaining the ability to perform multiple classification tasks in parallel.
3Measurement precision
If all colour planes are used for classification tasks, then classification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Different colour planes are provided at different quality levels or selectively to different classification services based on their specific requirements. For example, services that are colour-sensitive receive full colour plane data, while services that can work with luminance information receive only the Y plane or reduced colour data. This selective provision of colour information maintains classification accuracy for colour-critical tasks while improving overall processing speed by reducing redundant colour plane processing.
Data Source
AI summary
There is provided a system that, when in operation, classifies one or more elements within an input signal, wherein the system: receives a compressed version of the input signal, wherein the compressed version comprises at least two sets of compressed data in a hierarchy, wherein each set of compressed data, when decoded, enables the signal to be reconstructed up to a respective level of quality; decodes the compressed version of the signal up to a first level of quality by decoding a first set of compressed data to generate a first reconstructed signal; performs a first classification operation on the first reconstructed signal; decodes the compressed version of the signal up to a second level of quality by decoding a second set of compressed data to generate a second reconstructed signal; and performs one or more second classification operations on the second reconstructed signal.