Comparison of different methods of classification in subband coding of images

Joshi, Rajan; Jafarkhani, Hamid; Kasner, James H.; Fischer, Thomas R.; Farvardin, N.; Marcellin, Michael W.; Bamberger, R.H.

doi:10.1109/83.641409

Cited by 145 publications

(94 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While this approach precludes the use of any global information we show that competitive performance can be achieved because, as has been shown in 10,9,8,7], there exists signicant localization of energy within wavelet subbands, which c a n bee ciently exploited with context-based methods. We provide a complete description of our algorithm and highlight the modi cations that had to be undertaken with respect to our earlier context-based coding approaches 9] in order to compensate for the lack of global information.…”

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Line-based, reduced memory, wavelet image compression

Chrysafis

Ortega

2000

IEEE Trans. on Image Process.

273

View full text Add to dashboard Cite

This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether boththe wavelet transforms and the subsequent coding can be implemented in low memory without signi cant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is \line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. The main contributions of our work are two. First, we i n troduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a \normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coe cients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.

show abstract

Section: Introductionmentioning

confidence: 95%

“…Algorithms such as those in 4,5,6,7,8,9,10,11], are representative of the state of the art in wavelet coders. All of these algorithms assume that the wavelet transform (WT) for the whole image has been computed so that all the corresponding coe cients are available in the coding process.…”

Section: Introductionmentioning

confidence: 99%

Line-based, reduced memory, wavelet image compression

Chrysafis

Ortega

2000

IEEE Trans. on Image Process.

273

View full text Add to dashboard Cite

show abstract

“…DCT transform is applied for each block. Blocks are classified into three classes using block classification algorithm Equal Mean Normalized Standard Deviation (EMNSD) proposed by [11]. The modified EMNSD is representing in [12].…”

Section: Overview Of Systemmentioning

confidence: 99%

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Patel¹,

Patnaik²,

Mansi³

2016

IJCTE

View full text Add to dashboard Cite

Abstract-In image/video transmission system block or macro block classification has frequently been used to classify similar types of properties of block like spectral, statistical, perceptual etc. The bit allocation algorithm distribute total number of bits among a finite set of quantizer to maximize the quality. This paper proposed joint source channel coding (JSCC) algorithm for image transmission with: (i) optimum bit allocation for classified blocks (ii) Unequal Error Protection (UEP) at channel coding stage to minimize the total distortion. The bit allocation applied at two levels. First distribute allocate source rate between three different classes and second each class individual transforms coefficient bit assignment. The simulation results shows that using this algorithm approximately 3-4 dB improvement compare to conventional UEP and further 1.5-2 dB improvement using optimum bit allocation algorithm.

show abstract

“…This similarity in subband structure motivates us to investigate the suitability of well-established statistical models that were initially designed for scalar wavelets. In these models [9], [10], subbands' coefficients are modeled as Gaussian and generalized-Gaussian processes, respectively, with zero means and variances that depend on the coefficient location within each decomposition subband. In [9], it is assumed that the coefficients' variances belong to a finite set of values σ…”

Section: B Models Of Typical Imagesmentioning

confidence: 99%

High capacity watermarking using balanced multiwavelet transforms

Ghouti

Bouridane²,

Boussakta³

2005

IEEE International Conference on Image Processing 2005

View full text Add to dashboard Cite

Abstract-The emergence of digital multimedia and the proliferation of its use have raised major concerns about the protection of intellectual property. In response to these concerns, digital watermarks have emerged as a possible solution for protecting the intellectual property of digital content. In this paper, we derive estimates of data-hiding capacity of balanced multiwavelet transforms. This class of transforms, relatively new, has useful properties for image processing applications as shown in this paper. Furthermore, we will investigate the relevance of two closely related statistical models, developed for scalar wavelets, for modeling the statistics of balanced multiwavelet transform coefficients. Finally, we will present performance results of a spread spectrum watermarking system that is based on this new transform.

show abstract

Comparison of different methods of classification in subband coding of images

Cited by 145 publications

References 38 publications

Line-based, reduced memory, wavelet image compression

Line-based, reduced memory, wavelet image compression

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

High capacity watermarking using balanced multiwavelet transforms

Contact Info

Product

Resources

About