Embedded image coding using zerotrees of wavelet coefficients

Shapiro, J.M.

doi:10.1515/9781400827268.861

Cited by 583 publications

(866 citation statements)

References 0 publications

Supporting

Mentioning

848

Contrasting

Unclassified

Order By: Relevance

“…4. Finalmente, la imagen transformada se procesa con una de las técnicas existentes de búsqueda de los árboles de los coeficientes wavelets diferentes de cero [29] y se codifica con uno de los codificadores de entropía existentes [30].…”

Section: Modelo Propuesto De La Compresión De Imágenesunclassified

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

et al. 2016

View full text Add to dashboard Cite

Resumen-Se presenta un método para compresión de imágenes sin pérdidas, usando la transformada wavelet lifting con ajuste automático de coeficientes de filtros wavelets para una mayor compresión sin pérdidas. La propuesta se basa en reconocimiento de patrones utilizando clasificador 1-NN. Utilizando el reconocimiento de patrones se optimizan los coeficientes de los filtros lifting de manera global para cada imagen. La técnica propuesta fue aplicada para la compresión de imágenes de prueba y comparada con los filtros wavelets estándares CDF (2,2) y CDF (4,4), obteniendo resultados mejores en relación a la entropía obtenida para cada imágenes, así, como para el promedio general.Palabras clave-Compresión de imágenes sin pérdida, esquema lifting, wavelets, reconocimiento de patrones. Adjustment of Wavelet Filters for Image Compression Using Artificial IntelligenceAbstract-A method for lossless image compression using wavelet lifting transform with automatic adjustment of wavelet filter coefficients for better compression is presented. The proposal is based on pattern recognition by 1-NN classifier. Using the pattern recognition, the lifting filter coefficients are optimized globally for each image. The proposed technique was aplied to test images and the compression results were compared to the results produced by the standard CDF (2,2) and CDF (4,4) wavelet filters. The results obtained with the optimized wavelet filters are better in terms of the achieved entropy for all images, as well as for the overall performance.

show abstract

Section: Modelo Propuesto De La Compresión De Imágenesunclassified

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Figure 2 shows how the signal bandwidth is decomposed to 19 relatively critical subbands in the region of 0-7 kHz. The difference between our method and other similar ones is the combination of WP outputs in different levels of wavelet expansion tree wherever a perfect match with the critical bands is not possible (sub-bands [14][15][16][17][18][19]. These 19 critical sub-bands are summarized in Table 1 used the new wavelet kernel exploited in [6].…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

“…Decoder sets its amplitude to 1.5 × T or −1.5 × T while receiving POS or NEG, respectively. The two most important bits (included the sign bit) of binary representation of each significant coefficient are called the dominant bits, and the others are called the subordinate ones [19]. Subordinate bits are gradually measured and encoded during the Subordinate passes.…”

Section: Ezw and Modified Ezwmentioning

confidence: 99%

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Ghahabi

Savoji

2011

ISRN Signal Processing

View full text Add to dashboard Cite

A fast, efficient, and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising (1) embedded zero-tree wavelet (EZW) and (2) the set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.

show abstract

“…In addition, joint histograms of wavelet coefficients have B. Gooossens*, A. Pižurica and W. Philips are with the Department of Telecommunications and Information Processing (TELIN-IPI-IBBT), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium. Email: Bart.Goossens@telin.UGent.be, Aleksandra.Pizurica@telin.UGent.be, Wilfried Philips@telin.UGent.be, Tel: +32 9 264 7966, Fax: +32 9 264 4295. been studied in [10]- [19]. Taking advantage of correlations between wavelet coefficients either across space, scale or orientation, additional improvement in denoising performance is obtained.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

Goossens

Pižurica

Philips

2009

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-We propose a new statistical model for image restoration in which neighbourhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbourhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.

show abstract

Embedded image coding using zerotrees of wavelet coefficients

Cited by 583 publications

References 0 publications

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

Contact Info

Product

Resources

About