&lt;title&gt;Design of subband coders for high-quality images based on perceptual criteria&lt;/title&gt;

Algazi, V. Ralph; Ford, Gary E.; Mow, Matthew; Najmi, A.

doi:10.1117/12.158655

Cited by 5 publications

(3 citation statements)

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“…We de ne distortion factor F 3 as a function of two factor images, one each for the horizontal and vertical block error discontinuities. Thus, f 3h (m n) = I h (m n) 2 h (m n) (14) where h (m n) = e w (m n) e w (m n + 1) (15) and I h (m n) is an indicator function which selects only those di erences which span horizontal block boundaries. Now,…”

Section: D1 Distortion Factor F 3 (End Of Block Disturbances)mentioning

confidence: 99%

Objective picture quality scale (PQS) for image coding

Miyahara

Kotani

Algazi³

1998

IEEE Trans. Commun.

284

173

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| A new methodology for the determination of an objective metric for still image coding is reported. This methodology is applied to obtain a Picture Quality Scale (PQS) for the coding of achromatic images over the full range of image quality de ned by the subjective Mean Opinion Score (MOS). This Picture Quality Scale takes into account the properties of visual perception for b o t hglobal features and localized disturbances. PQS closely approximates the MOS, with a correlation coe cient of more than 0.92, as compared to 0.57 obtained using the conventional WMSE. Extensions and applications of the methodology and of the resulting metric are discussed.Keywords| Image compression, image quality, quality metrics, psychophysics.

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Section: D1 Distortion Factor F 3 (End Of Block Disturbances)mentioning

confidence: 99%

Objective picture quality scale (PQS) for image coding

Miyahara

Kotani

Algazi³

1998

IEEE Trans. Commun.

284

173

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“…We h a ve f o u n d that adaptive noise reduction using anisotropic di usion can substantially reduce the noise while maintaining the structured image details, important in the perception of image quality. 1,8 In adaptive noise reduction, an interactive data dependent ltering algorithm is used. It can be shown that ltering with a family of Gaussian lter kernels G(x y t) w i t h v ariance parameter t, i.e.…”

Section: Adaptive Noise Reductionmentioning

confidence: 99%

<title>Progressive perceptually transparent coder for very high quality images</title>

et al. 1994

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In the perceptually transparent coding of images, we use representation and quantization strategies that exploit properties of human perception to obtain an approximate digital image indistinguishable from the original. This image is then encoded in an error free manner. The resulting coders have better performance than error free coding for a comparable quality. F urther, by considering changes to images that do not produce perceptible distortion, we i d e n tify image characteristics onerous for the encoder, but perceptually unimportant. One such characteristic is the typical noise level, often imperceptible, encountered in still images. Thus, we consider adaptive noise removal to improve coder performance, without perceptible degradation of quality. In this paper, several elements contribute to coding e ciency while preserving image quality: adaptive noise removal, additive decomposition of the image with a high activity remainder, coarse quantization of the remainder, progressive representation of the remainder, using bilinear or directional interpolation methods, and e cient encoding of the sparse remainder. The overall coding performance improvement due to noise removal and the use of a progressive code is about 18%, as compared to our previous results for perceptually transparent coders. The compression ratio for a set of nine test images is 3.72 for no perceptible loss of quality.

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“…6 We h a ve found, however, that anisotropic di usion based adaptive noise reduction techniques can substantially reduce this noise while maintaining the structured image details, important in the perception of image quality. 2,9…”

Section: Adaptive Noise Reductionmentioning

confidence: 99%

<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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<title>Design of subband coders for high-quality images based on perceptual criteria</title>

Cited by 5 publications

References 0 publications

Objective picture quality scale (PQS) for image coding

Objective picture quality scale (PQS) for image coding

<title>Progressive perceptually transparent coder for very high quality images</title>

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

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