Region-based image coding with multiple algorithms

Petrou, Maria; Hou, Peixin; Kamata, Sei‐ichiro; Underwood, Craig

doi:10.1109/36.911114

Cited by 17 publications

(14 citation statements)

References 14 publications

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“…Maria Petrou et al [20] explored various compression algorithms like JPEG, SPIHT, object based wavelet method etc. Each algorithm has its own significance on different types of regions.…”

Section: Adaptive Encoding Algorithmmentioning

confidence: 99%

“…Let R represent the entire image region and a predicate [20] is selected. The predicate is based on the mean vector, covariance matrix etc.…”

Section: Segmentation By Region Splitting and Mergingmentioning

confidence: 99%

“…Moreover, for better performance, the different regions of the image are coded by means of different encoding techniques, say, SPIHT algorithm for relatively uniform regions and modified Object Wavelet method for highly textured areas, which are quite common in remote-sensing images [20].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Encoding Algorithm for Multispectral Images

Deepa¹

2010

IJCA

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ABSTRACT-A new adaptive multispectral imagecompression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class of the region, appropriate encoding technique is chosen. The image is first segmented by means of Region splitting and merging procedure based on the statistical characteristic of the image. Then class adaptive hotelling transform or Karhunen Loeve transform (KLT) in the spectral domain and the shape adaptive wavelet transform in the spatial domain are adopted in the image by considering the spatial, spectral and statistical properties which are unique to the multispectral images. The quadtree is used for determining the transform block size and a single KLT matrix is used for the regions of same class ie., class adaptive KLT is applied and the transformation is followed by shape adaptive wavelet transform(SAWT) incorporating the spatial and structural properties of the multispectral image. After transformation, based on the regions identified, if the region is relatively uniform or smooth, the SPIHT (Set Partitioning in Hierarchical Trees) algorithm is adopted. If not, that is, if the region is highly textured in nature, then object based wavelet method is used for compression. Thus the advantages of both SPIHT algorithm and object based wavelet encoding method, both in terms of visual quality and PSNR values, are incorporated in a single compression technique.

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“…Maria Petrou et al [20] explored various compression algorithms like JPEG, SPIHT, object based wavelet method etc. Each algorithm has its own significance on different types of regions.…”

Section: Adaptive Encoding Algorithmmentioning

confidence: 99%

“…Let R represent the entire image region and a predicate [20] is selected. The predicate is based on the mean vector, covariance matrix etc.…”

Section: Segmentation By Region Splitting and Mergingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Encoding Algorithm for Multispectral Images

Deepa¹

2010

IJCA

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“…The consequence is that spatial encoding of a class is rather cumbersome and not very efficient, not to mention that the region-based description of the image is lost. In [24], a truly region-based coder is proposed which, in addition, has the ability to switch coding techniques depending on the region statistics. Despite the interesting approach, implementation presents several weak points that limit its performance and overall value, notably, the manual segmentation of images, and the use of region coding techniques far from the state of the art.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Transform Coding of Multispectral Images

Cagnazzo

Poggi

Verdoliva

2007

IEEE Trans. on Image Process.

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Abstract-We propose a new efficient region-based scheme for the compression of multispectral remote-sensing images. The region-based description of an image comprises a segmentation map, which singles out the relevant regions and provides their main features, followed by the detailed (possibly lossless) description of each region. The map conveys information on the image structure and could even be the only item of interest for the user; moreover, it enables the user to perform a selective download of the regions of interest, or can be used for high-level data mining and retrieval applications. This approach, with the multiple pieces of information required, may seem inherently inefficient. The goal of this research is to show that, by carefully selecting the appropriate segmentation and coding tools, region-based compression of multispectral images can be also effective in a rate-distortion sense, thus providing an image description that is both insightful and efficient. To this end, we define a generic coding scheme, based on Bayesian image segmentation and on transform coding, where several key design choices, however, are left open for optimization, from the type of transform, to the rate allocation procedure, and so on. Then, through an extensive experimental phase on real-world multispectral images, we gain insight on such key choices, and finally single out an efficient and robust coding scheme, with Bayesian segmentation, class-adaptive Karhunen-Loève spectral transform, and shape-adaptive wavelet spatial transform, which outperforms state-of-the-art and carefully tuned conventional techniques, such as JPEG-2000 multicomponent or SPIHT-based coders.

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“…More relevant to the problem is the literature on the compression of multispectral images, where techniques for the joint analysis and compression of images are currently under investigation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Compression of multitemporal remote sensing images through Bayesian segmentation

Cagnazzo¹,

Poggi²,

Scarpa³

et al.

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings

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Abstract-Multitemporal remote sensing images are useful tools for many applications in natural resource management. Compression of this kind of data is an issue of interest, yet, only a few paper address it specifically, while general-purpose compression algorithms are not well suited to the problem, as they do not exploit the strong correlation among images of a multitemporal set of data.Here we propose a coding architecture for multitemporal images, which takes advantage of segmentation in order to compress data. Segmentation subdivides images into homogeneous regions, which can be efficiently and independently encoded. Moreover this architecture provides the user with a great flexibility in transmitting and retrieving only data of interest.

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Region-based image coding with multiple algorithms

Cited by 17 publications

References 14 publications

Adaptive Encoding Algorithm for Multispectral Images

Adaptive Encoding Algorithm for Multispectral Images

Region-Based Transform Coding of Multispectral Images

Compression of multitemporal remote sensing images through Bayesian segmentation

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