Optimum classification in subband coding of images

Joshi, Rajan; Fischer, Thomas R.; Bamberger, R.H.

doi:10.1109/icip.1994.413482

Cited by 43 publications

(26 citation statements)

References 8 publications

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“…Tse (Tse, 1992) improved the classification performance by having different size classes. Joshi, Fischer and Bamberger (Joshi, 1994) investigated this type of classification technique. They optimized the classification method by maximising classification gain (Joshi, 1994), and applied this technique to subband coding.…”

Section: 4 Discrete Cosine Transform (Dct)mentioning

confidence: 99%

“…Joshi, Fischer and Bamberger (Joshi, 1994) investigated this type of classification technique. They optimized the classification method by maximising classification gain (Joshi, 1994), and applied this technique to subband coding. Jafarkhani, Farvardin and Lee (Jafarkhani, 1994) modified the Chen-Smith classification method for the discrete wavelet transform coding of images.…”

Section: 4 Discrete Cosine Transform (Dct)mentioning

confidence: 99%

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Image Classification Technique using Modified Particle Swarm Optimization

Shukran¹,

Chung²,

Yeh³

et al. 2011

MAS

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Image classification is becoming ever more important as the amount of available multimedia data increases. With the rapid growth in the number of images, there is an increasing demand for effective and efficient image indexing mechanisms. For large image databases, successful image indexing will greatly improve the efficiency of content based image classification. One attempt to solve the image indexing problem is using image classification to get high-level concepts. In such systems, an image is usually represented by various low-level features, and high-level concepts are learned from these features. PSO has recently attracted growing research interest due to its ability to learn with small samples and to optimize high-dimensional data. Therefore, this paper will introduce the related work on image feature extraction. Then, several techniques of image feature extraction will be introduced which include two main methods. These methods are RGB and Discrete Cosine Transformation (DCT). Finally, several experimental designs and results concerning the application of the proposed image classification using modified PSO classifier will be described in detail.

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Section: 4 Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Section: 4 Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Image Classification Technique using Modified Particle Swarm Optimization

Shukran¹,

Chung²,

Yeh³

et al. 2011

MAS

View full text Add to dashboard Cite

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“…The algorithms, known as EMNSD [15], works as follows. Assume after dividing into blocks of size 8 × 8 there are N image blocks to be classified.…”

Section: Image Classificationmentioning

confidence: 99%

“…This activity level can be specified in terms of σ j . Since comparison of standard deviation of classes having different mean being meaningless, coefficient of variation or the Mean Normalised Standard Deviation (MNSD) is used as the criterion of classification [15].…”

Section: Image Classificationmentioning

confidence: 99%

“…Later, it is shown that the classiCopyright c 2015 The Institute of Electronics, Information and Communication Engineers fication gain can be maximised by having non-uniform classification [14]. In [15] sub-band coding of the wavelet coefficients of the image is done after classifying into different classes. In this approach Equal Mean Normalised Standard Deviation(EMNSD) algorithm can be used for finding the number of blocks in each class such that the resulting classes have similar statistical properties.…”

Section: Introductionmentioning

confidence: 99%

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Application of Content Specific Dictionaries in Still Image Coding

Patel

Jose

Patnaik

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThe concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression. key words: image compression, JPEG, sparse coding, K-SVD dictionary learning, OMP

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Coding of Hyperspectral Imagery with Trellis-Coded Quantization

Abousleman

Hyperspectral Data Compression

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Optimum classification in subband coding of images

Cited by 43 publications

References 8 publications

Image Classification Technique using Modified Particle Swarm Optimization

Image Classification Technique using Modified Particle Swarm Optimization

Application of Content Specific Dictionaries in Still Image Coding

Coding of Hyperspectral Imagery with Trellis-Coded Quantization

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