A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)

By Niranjan D. Narvekar, Lina Karam. IEEE Transactions on Image Processing, 2011

600 citations recorded by the source corpus.

This paper presents a no-reference image blur metric that is based on the study of human blur perception for varying contrast values. The metric utilizes a probabilistic model to estimate the probability of detecting blur at each edge in the image, and then the information is pooled by computing the cumulative probability of blur detection (CPBD). The performance of the metric is demonstated by comparing it with existing no-reference sharpness/blurriness metrics for various publicly available image databases.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/748fb9dd110622399bd5ed6903066d007614183a

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Citations in the corpus

The Konstanz natural video database (KoNViD-1k) (2017, background): “1) Blur: The blur of a frame was assessed by the cumulative probability of blur detection (CPBD) metric [5].”
Flow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset (2021, methodology): “PSNR, SSIM and CPBD[27] are utilized as metrics to measure the visual quality.”
A no-reference metric for evaluating the quality of motion deblurring (2013, background): “• Cumulative Probability of Blur Detection (CPBD) is a no-reference sharpness measure proposed by [Narvekar and Karam 2011].”
No-Reference Image Sharpness Assessment in Autoregressive Parameter Space (2015, methodology): “They are: 1) Three FR IQA models, peak signal-to-noise ratio (PSNR) that computes the signal energy preservation, structural similarity index (SSIM) that compares luminance, contrast and structural similarities [1], and VIF [2]; 2) Six NR IQA models, NFEQM [22], DIIVINE [24], BLIINDS-II [25], BRISQU”
Using Free Energy Principle For Blind Image Quality Assessment (2015, methodology): “We first evaluate the performance of the proposed NFERM with a total number of fifteen IQA approaches on the LIVE database [39]: 1) classical FR PSNR, SSIM [4], MS-SSIM [5]; 2) popular blind distortion-specific WNJE [21], MBBM [22], Sheikh [23], SINE [24], JNB [26], CPBD [27], NFEQM [17]; 3) state-o”
Blind Quality Assessment Based on Pseudo-Reference Image (2018, background): “Ferzli and Karam [20] introduced a concept of just noticeable blur (JNB), which described the minimum blurring needed to be perceived near the edge given a contrast higher than the just noticeable difference (JND).”
Lip Movements Generation at a Glance (2018, methodology): “To evaluate the sharpness of the generated image frames, we compute the perceptualbased no-reference objective image sharpness metric (CPBD) [21].”
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (2022, methodology): “We employ Frechet In-ception Distance (FID) [13,33] and cumulative probability blur detection (CPBD) [25] to evaluate the quality of the images.”