Download Image Processing and Communications Challenges 8: 8th by Ryszard S. Choraś PDF

By Ryszard S. Choraś

This ebook collects a chain of study papers within the quarter of snapshot Processing and Communications which not just introduce a precis of present know-how but in addition provide an outlook of strength function difficulties during this quarter. the major goal of the ebook is to supply a suite of accomplished references on a few fresh theoretical improvement in addition to novel functions in photo processing and communications. The booklet is split into components and provides the complaints of the 8th foreign photo Processing and Communications convention (IP&C 2016) held in Bydgoszcz, Poland September 7-9 2016. half I offers with photograph processing. A finished survey of alternative tools of photo processing, machine imaginative and prescient is additionally awarded. half II bargains with the telecommunications networks and laptop networks. functions in those components are considered.

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Read or Download Image Processing and Communications Challenges 8: 8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings PDF

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Additional resources for Image Processing and Communications Challenges 8: 8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings

Example text

Suppose we filter the noisy input image I by means of NLM and obtain the output image O. The NLM method is based on the following Eq. [2] w(p, q)I(q) O(p) = (1) q∈Ω where O(p) is the calculated brightness at pixel p, and Ω is some area in the image. The I(q) is the unfiltered brightness of a pixel q in the neighborhood of the pixel p in the input image I, and the weight w(p, q) is calculated using the Gaussian weighting function w(p, q) = ||B(p) − B(q)||2 1 exp − Z(p) h2 (2) In this equation h is a filtering parameter, and B(p) and B(q) denote the vectors containing the brightness of neighboring pixels centered around p and q, respectively, hence ||B(p)−B(q)|| is the Euclidean distance between the two brightness vectors B(p) and B(q).

ICCVG 2014. LNCS, vol. 8671, pp. 212–219. Springer, Heidelberg (2014). 1007/978-3-319-11331-9 26 4. : Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition (ICPR), pp. 2366–2369. IEEE (2010) 5. IJG: Independent JPEG Group (2015). org/ 6. : A distortion measure for blocking artifacts in images based on human visual sensitivity. IEEE Trans. Image Process. 4(6), 713–724 (1995) 7. : Effects of JPEG compression on image classification. Int. J. Remote Sens. 24(7), 1535–1544 (2003) 8.

405 images are selected randomly, where the dimensions are not smaller than Quality Prediction of Compressed Images via Classification 39 1024 × 768 pixels. For assignment of the images to classes (see Fig. 1, Training (1)), they are compressed by the JPEG algorithm with a low value of QF = 30, and the quality of the compressed images are computed. 3. Then the images are assigned to one of two classes (l = 2) by the following way: – A set of 200 images with the highest SSIM values and a set of 200 images with the highest PSNR are selected.

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