Enhancement of region-of-interest coded images by using adaptive regularization

Jung, Junghoon; Joung, Shichang; Jang, Younhui; Paik, Joonki

doi:10.1109/icce.2000.854497

2000 Digest of Technical Papers. International Conference on Consumer Electronics. Nineteenth in the Series (Cat. No.00CH37102)

DOI: 10.1109/icce.2000.854497

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Enhancement of region-of-interest coded images by using adaptive regularization

Junghoon Jung

Shichang Joung²,

Younhui Jang³

et al.

Abstract: We propose a new algorithm that post-processes waveletbased ROI coded images by using adaptive iterative regualrization. The proposed algorithm can efficiently reduce the ringing artifacts as well as restore the details of the image. The algorithm can be used in still iamge compression standards, videophone, video conferencing, and other visual communication systems.

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Cited by 4 publications

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Restoration of wavelet-compressed images using adaptively masking smoothness constraints

Jung

Paik²

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)

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The wavelet-compressed images suffer from coding artifacts, such as ringing and blurring, due toe quantizing the transform coefficients. To reduce such coding artifacts, we propose a new image enhancement algorithm based on regularized iterative image restoration. The proposed algorithm uses multiple constraints to simultaneously suppress the coding artifacts and preserve the original high-frequnecy components. We also present the hierarchical implementation for the proposed algorithm. Spatial adaptivity plays a role in keeping edge from spreading out to other pixels which have different directional characteristics.

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Restoration of wavelet-compressed images using adaptively masking smoothness constraints

Jung

Paik²

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)

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Wavelet-based foveated image quality measurement for region of interest image coding

Wang

Bovik

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)

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Region of interest (ROI) image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Most image quality metrics have been developed for uniform resolution images. These metrics are not appropriate for the assessment of ROI coded images, where space-variant resolution is necessary. The spatial resolution of the human visual system (HVS) is highest around the point of fixation and decreases rapidly with increasing eccentricity. Since the ROIs are usually the regions "fixated" by human eyes, the foveation property of the HVS supplies a natural approach for guiding the design of ROI image quality measurement algorithms. We have developed an objective quality metric for ROI coded images in the wavelet transform domain. This metric can serve to mediate the compression and enhancement of ROI coded images and videos. We show its effectiveness by applying it to an embedded foveated image coding system.

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Weed Detection utilizing Quadratic Polynomial and ROI Techniques

Aris

Mokri

Mustafa

et al. 2007

2007 5th Student Conference on Research and Development

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Machine vision for selective weeding or selective herbicide spraying relies substantially on the ability of the system to analyze weed images and process the extracted knowledge for decision making prior to implementing the identified control action. To control weed, different weed type would require different herbicide formulation. Consequently the weed must be identified and classified accordingly. In this work, weed images were classified as either broad or narrow weed type. A fundamental problem in weed image recognition using planar curve analysis is to detect curve. It is difficult to successfully extract curve from the image of weed edges since the appropriate scale to use for extraction is not known a priori As such, this paper considers a curve detection method based on the quadratic polynomial technique which include the use of the region-of-interests (ROI) technique. The ROI technique creates image subsets by selecting regions of the displayed image. The ROIs are typically used to extract statistics for image operations such as classification. As such, the objective of this paper is to present a novel application of curve detection feature extraction technique in weed classification.

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Enhancement of region-of-interest coded images by using adaptive regularization

Cited by 4 publications

References 4 publications

Restoration of wavelet-compressed images using adaptively masking smoothness constraints

Restoration of wavelet-compressed images using adaptively masking smoothness constraints

Wavelet-based foveated image quality measurement for region of interest image coding

Weed Detection utilizing Quadratic Polynomial and ROI Techniques

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