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Image Quality Analysis, Enhancement and Optimization Techniques for Computer Vision

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This article explains the differences between images intended for human viewing and for computer analysis, and how these differences factor into the hardware and software design of a camera intended for computer vision applications versus traditional still and video image capture. It discusses various methods, both industry standard and proprietary, for assessing and optimizing computer vision-intended image quality with a given camera subsystem design approach. And it introduces an industry alliance available to help product creators incorporate robust image capture and analysis capabilities into their computer vision designs.

The bulk of historical industry attention on still and video image capture and processing has focused on hardware and software techniques that improve images' perceived quality as evaluated by the human visual system (eyes, optic nerves, and brain), thereby also mimicking as closely as possible the human visual system. However, while some of the methods implemented in the conventional image capture-and-processing arena may be equally beneficial for computer vision processing purposes, others may be ineffective, thereby wasting available processing resources, power consumption, etc.

Some conventional enhancement methods may, in fact, be counterproductive for computer vision. Consider, for example, an edge-smoothing or facial blemish-suppressing algorithm: while the human eye might prefer the result, it may hamper the efforts of a vision processor that's searching for objects in a scene or doing various facial analysis tasks. In contrast, various image optimization techniques for computer vision might generate outputs that the human eye and brain would judge as "ugly" but a vision processor would conversely perceive as "improved."

Historically, the computer vision market was niche, thereby forcing product developers to either employ non-optimal hardware and software originally intended for traditional image capture and processing (such as a smartphone camera module, tuned for human perception needs) or to create application-optimized products that by virtue of their low volumes had consequent high costs and prices. Now, however, the high performance, cost effectiveness, low power consumption, and compact form factor of various vision processing technologies are making it possible to incorporate practical computer vision capabilities into a diversity of products that aren't hampered by the shortcomings of historical image capture and processing optimizations.

Detailing the Differences

To paraphrase a well-known proverb, IQ (image quality) is in the eye of the beholder…whether that beholder is a person or a computer vision system. For human perception purposes...