How to Measure the Quality of Images When You Don't Have Gold Standard Images to Compare
In the realm of image analysis and processing, quantifying image quality forms a crucial cornerstone of various methodologies. This is especially true when there's a lack of gold standard images or ground truth to set the benchmark. In such scenarios, we're often left asking: how can we measure image quality objectively or subjectively?
Let's delve into this topic with an emphasis on establishing robust and reliable metrics for image quality analysis.
Objective Measurement of Image Quality Without Ground Truth
Objective quality metrics quantify the difference between two images based on numerical methods. But, how do we assess image quality objectively when we don't have a ground truth image? One of the most promising methods revolves around Blind/No-Reference metrics.
No-Reference Image Quality Assessment (NR-IQA)
No-Reference Image Quality Assessment (NR-IQA) algorithms are designed to predict perceived image quality without referring to the original, clean image. They're beneficial when the original is unavailable or nonexistent. Among NR-IQA methods, some notable ones include:
- Naturalness Image Quality Evaluator (NIQE): NIQE estimates image quality without requiring any explicit knowledge about the type of distortion in the image, or any statistical information about the noise. Instead, it uses natural scene statistics extracted from undistorted images to predict image quality.
- Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE): BRISQUE extracts features from the distorted image that represent changes in natural scene statistics. By comparing these features to those of undistorted images, it computes a quality score.
- Perceptual Evaluation of Video Quality (PEVQ): While originally developed for video, PEVQ can be used for still images, assessing their perceptual quality based on human visual perception.
Subjective Measurement of Image Quality Without Ground Truth
Subjective measurements rely on human opinion and perception to assess the quality of images. When a ground truth is not available, we turn to User Studies and Opinion Scoring.
User Studies
User studies involve having multiple individuals rate the quality of images. Through consensus, we can derive a relative quality metric, acknowledging that subjectivity can lead to variations in the evaluation. It's a versatile method, especially when dealing with diverse and complex image distortions where objective metrics may struggle.
Opinion Scoring
Opinion scoring is another form of subjective quality assessment, usually deployed in a controlled environment. Multiple observers rate the quality of images on a predefined scale, like a Likert scale. The median or mean score from this analysis forms the subjective image quality score.
Measuring Image Quality with Gold Standard Images
Having gold standard images simplifies the process of quality assessment. We can utilize a series of both objective and subjective metrics when comparing processed images to a known benchmark.
Objective Measurements with Gold Standard Images
Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are common metrics for objective assessment. They measure the average squared difference and the square root of the MSE respectively between the pixel intensity values of the benchmark and the processed image. A lower MSE and RMSE signify better image quality.
Peak Signal to Noise Ratio (PSNR) is another objective metric, which represents the ratio between the maximum possible power of an image and the corrupting noise affecting its quality. A higher PSNR is indicative of superior image quality.
Subjective Measurements with Gold Standard Images
With ground truth images, we can also employ Double Stimulus Continuous Quality Scale (DSCQS), Double Stimulus Impairment Scale (DSIS), or Single Stimulus (SS) methods for subjective assessment. They involve observers viewing and comparing a sequence of original and processed images and giving a quality or impairment score, offering a clear indication of perceived quality differences.
Measuring image quality accurately, whether or not a ground truth exists, is of paramount importance in many fields. Utilizing the methods we've outlined provides a solid foundation for gauging image quality and thus, improving our image processing algorithms.
I hope it helps in improving your understanding.
The blog provides a comprehensive overview of different methodologies for quantifying and assessing image quality. It explores both objective and subjective measurements, highlighting their significance in scenarios where ground truth images may be absent.
ReplyDeleteThe introduction emphasizes the importance of establishing robust metrics for image quality analysis, particularly in the absence of gold standard or reference images. The subsequent discussion on no-reference image quality assessment (NR-IQA) methods, such as NIQE, BRISQUE, and PEVQ, showcases the advancements in predicting perceived image quality without explicit knowledge of distortion or statistical information.
The blog also delves into subjective measurements, which rely on human opinion and perception. It explains the use of user studies, where multiple individuals rate image quality, and opinion scoring, where observers provide quality scores on a predefined scale. These subjective approaches offer valuable insights, especially in complex scenarios where objective metrics may struggle to capture the nuances of image distortions.
Overall, the blog provides a comprehensive and informative overview of different measurement methodologies for image quality assessment. It effectively highlights the significance of objective and subjective metrics, as well as the role of gold standard images, in accurately gauging image quality and improving image processing techniques.
The measurement of image quality plays a vital role in various image analysis and processing methodologies. However, it becomes challenging when there is a lack of ground truth or reference images to set a benchmark. In such scenarios, the focus shifts towards objective and subjective metrics for assessing image quality.
ReplyDeleteObjective metrics, such as Blind/No-Reference algorithms, aim to quantify image quality based on numerical methods without requiring a clean reference image. On the other hand, subjective metrics rely on human perception and opinion, utilizing user studies or opinion scoring to evaluate image quality. While objective metrics provide numerical assessments, subjective metrics capture the human aspect of image quality evaluation.
By employing a combination of objective and subjective approaches, researchers and practitioners can gain valuable insights into the quality of images and enhance their image processing techniques.
This blog provides insights into measuring the quality of images when gold standard images are not available. It discusses objective measurement methods such as blind/no-reference metrics like NIQE and BRISQUE, as well as subjective measurement approaches like user studies and opinion scoring. Additionally, it covers objective metrics like MSE, RMSE, and PSNR, and subjective assessments using methods like DSCQS, DSIS, or SS when gold standard images are present. Overall, the blog offers valuable techniques for objectively and subjectively evaluating image quality, which is crucial for image analysis and processing.
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