Key point
・Algorithm that uses neural networks to output image quality evaluation with the same precision as human subjectivity.
・Immediate output and analyze image quality degradation factors such as light effects (shadows, lighting, etc.) and compression.
・Applicable to video and audio quality evaluation, such as the applications to image inspections, onboard cameras, robot vision, etc.
Benefit
The advantages of noise reduction/removal technologies developed in this laboratory are as follows.
1) An image quality evaluation equivalent to human subjectivity can be outputted immediately.
2）You can also create a degradation list of image quality (24 types) and identify under which list the subject falls under. For example, you can identify deterioration factors such as whether the image has noise or is quantized, or whether if it is blurred or compressed.
3）PSNR is the most well-known technique for image quality evaluation, and other techniques are available as well. In PSNR, both PLCC and SROCC are 0.639, but in our proposed method,
PLCC is 0.981 and SROCC is 0.977, which are very close to human subjective values.
1) An image quality evaluation equivalent to human subjectivity can be outputted immediately.
2）You can also create a degradation list of image quality (24 types) and identify under which list the subject falls under. For example, you can identify deterioration factors such as whether the image has noise or is quantized, or whether if it is blurred or compressed.
3）PSNR is the most well-known technique for image quality evaluation, and other techniques are available as well. In PSNR, both PLCC and SROCC are 0.639, but in our proposed method,
PLCC is 0.981 and SROCC is 0.977, which are very close to human subjective values.
Market Application
The technology can be used to evaluate image quality. Also, in addition to still images, it can be applied to the evaluation of video, audio, and so on.
For example, it is possible to quantify the degree of light effect (e.g., lighting and shadows) at the time of imaging and analyze its reasons, apply on the uses that require high-quality imaging (e.g., medical images, etc.), and monitor whether there is a problem with the image taken during infrastructure periodic examinations.
It is also possible to narrow down only the images with good quality when dealing with large amounts of image data. Moreover, for videos, it can be applied to the case where the shooting environment changes dynamically, such as when mounted on an onboard camera or when attached to a robot (robot vision.)
For example, it is possible to quantify the degree of light effect (e.g., lighting and shadows) at the time of imaging and analyze its reasons, apply on the uses that require high-quality imaging (e.g., medical images, etc.), and monitor whether there is a problem with the image taken during infrastructure periodic examinations.
It is also possible to narrow down only the images with good quality when dealing with large amounts of image data. Moreover, for videos, it can be applied to the case where the shooting environment changes dynamically, such as when mounted on an onboard camera or when attached to a robot (robot vision.)