&lt;title&gt;Invariance of edges and corners under mean-curvature diffusions of images&lt;/title&gt;

El-Fallah, A.; Ford, Gary E.; Algazi, V. Ralph; Estes, Robert R.

doi:10.1117/12.205467

Cited by 14 publications

(9 citation statements)

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“…Using this lter, denoted the corner preserving lter (CPF), more iterations are allowed (yielding more noise reduction) while still maintaining perceptual transparency. 8 Ten CPF iterations results in more than 10 dB of noise reduction in the at portions of the image (as can been seen in Table 1), while introducing no perceptible changes | as long as the PSNR of the original image is at least 46 dB, which implies that the noise is not perceptible. However, the performance of the coder is substantially improved, as discussed below.…”

Section: Anisotropic DI Usionmentioning

confidence: 87%

<title>Progressive analysis-based perceptually transparent coder for still images</title>

Algazi

Ford

Estes

et al. 1996

Very High Resolution and Quality Imaging

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The encoding of images at high quality is important i n a n umber of applications. We h a ve d e v eloped an approach to coding that produces no visible degradation and that we denote as perceptually transparent. S u c h a technique achieves a modest compression, but still signi cantly higher than error free codes. Maintaining image quality is not important in the early stages of a progressive s c heme, when only a reduced resolution preview is needed. In this paper, we describe a new method for the progressive transmission of high quality still images, that e ciently uses the lower resolution images in the encoding process. Analysis based interpolation is used to estimate the higher resolution image, and reduces the incremental information transmitted at each step. This methodology for high quality image compression is also aimed at obtaining a compressed image of higher perceived quality that the original.

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Section: Anisotropic DI Usionmentioning

confidence: 87%

<title>Progressive analysis-based perceptually transparent coder for still images</title>

Algazi

Ford

Estes

et al. 1996

Very High Resolution and Quality Imaging

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“…(4) In our approach to inhomogeneous diffusion, the diffusion coefficient is the inverse of the surface gradient magnitude, i.e., 1 (5) In terms of nnii normal vector field on a neighborhood of p and we obtain =V.N=+=2H (7) at ax1 3x2 where the mean curvanre H is the average value of the normal curvature in any two orthogonal directions. (4) In our approach to inhomogeneous diffusion, the diffusion coefficient is the inverse of the surface gradient magnitude, i.e., 1 (5) In terms of nnii normal vector field on a neighborhood of p and we obtain =V.N=+=2H (7) at ax1 3x2 where the mean curvanre H is the average value of the normal curvature in any two orthogonal directions.…”

Section: Mean Curvature Diffusion (Mcd)mentioning

confidence: 98%

<title>Preprocessing for improved performance in image and video coding</title>

et al. 1995

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In previous work, we have reported on the benefits of noise reduction prior to coding of very high quality images. Perceptual transparency can be achieved with a significant improvement in compression as compared to error free codes. In this paper, we examine the benefits of pre-processing when the quality requirements are not very high, and perceptible distortion results. The use of data dependent anisotropic diffusion that maintains image structure, edges, and transitions in luminance or color is beneficial in controlling the spatial distribution of errors introduced by coding. Thus, the merit of preprocessing is for the control of coding errors. In this preliminary study, we only consider preprocessing prior to the use of the standard JPEG and MPEG coding techniques.Keywords: image coding, video coding, image processing, inhomogeneous diffusion.We study the pre-processing of images and video in two ways. First, by examining the effect of pre-processing on still image quality, as measured by a recently developed perceptually based picture quality scale (PQS). Second, we examine the effect of pre-processing on video coding performance, where noise has a more substantial effect, because the redundancy reduction sought by interframe coding is limited by the unpredictability of the noise from frame to frame. We evaluate the bit rates of encoded pre-processed and original video and compare for the same bit rate the quality of the resulting images either subjectively, using the PSNR, or objectively. We also examine briefly the benefits of post processing . We show an example of reduction of the blocking effect prevalent in DCT that is incorporated in the JPEG and MPEG standards. 22 /SPIE Vol. 2564 O-8194-1923-O/95/$6.OO Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/16/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx SPIE Vol. 2564 /27 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/16/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…This characteristic may allow only a very small compression in order to maintain acceptable image quality. One approach to a better control of image quality, is to pre-process the image in an adaptive fashion so as to introduce imperceptible or controlled degradations to the image 12,14]. In previous work using this approach, an e ective perceptually transparent coder was designed 6].…”

Section: Introductionmentioning

confidence: 99%

“…The key to making such an alternative approach to image coding possible is the use of methods that provide the necessary local control of image degradation. One recently developed technique, based on inhomogeneous diffusion 12,14], does so e ectively.…”

Section: Introductionmentioning

confidence: 99%

Quality measurement and use of pre-processing in image compression

Algazi¹,

Avadhanam²,

Estes³

1998

Signal Processing

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Traditional quality measures for image coding, such as the p e a ksignal to noise ratio, assume that the preservation of the original image is the desired goal. However, pre-processing images prior to encoding, designed to remove noise or unimportant detail, can improve the overall performance of an image coder. Objective image quality metrics obtained from the di erence b e t ween the original and coded images cannot properly assess this improved performance. This paper proposes a new methodology for quality metrics that di erentially weighs the changes in the image due to pre-processing and encoding. These new quality measures establish the value of pre-processing for image coding and quantitatively determine the performance improvement that can b ethus achieved by JPEG and wavelet coders.

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<title>Invariance of edges and corners under mean-curvature diffusions of images</title>

Cited by 14 publications

References 0 publications

<title>Progressive analysis-based perceptually transparent coder for still images</title>

<title>Progressive analysis-based perceptually transparent coder for still images</title>

<title>Preprocessing for improved performance in image and video coding</title>

Quality measurement and use of pre-processing in image compression

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