Efficient image coding using multiresolution wavelet transform and vector quantization

Pemmaraju, Suryalakshmi; Mitra, Sunanda

doi:10.1109/iai.1996.493741

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005;Premaraju, 1996).…”

mentioning

confidence: 82%

Design of a neuro fuzzy model for image compression in wavelet domain

Singh

Rajpal

Murthy

2009

J Indian Soc Remote Sens

View full text Add to dashboard Cite

Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in the data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector quantization, wavelet-based sub band decomposition and neural network. The vector quantization is often used when high compression ratios are required. The implementation consists of three steps: first, the image is decomposed into a set of sub bands with different resolutions corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. In the second step, wavelet coefficients corresponding to the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. Finally, the result of the second step was used as input to fuzzy vector quantizer. Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005;Premaraju, 1996).

show abstract

mentioning

confidence: 82%

Design of a neuro fuzzy model for image compression in wavelet domain

Singh

Rajpal

Murthy

2009

J Indian Soc Remote Sens

View full text Add to dashboard Cite

show abstract

“…Image compression algorithms based on discrete wavelet transform (DWT) [1], such as embedded zero wavelet (EZW) [2] and set partitioning in hierarchical trees(SPIHT) [3] provide excellent rate distortion performance with low encoding complexity. However, it is quite fragile against bit errors in wireless channels because a single bit error within SPIHT streams may cause misinterpretation of the later bits, and then leads to error propagation and possible loss of synchronization.…”

Section: Introductionmentioning

confidence: 99%

A Modified SPIHT Algorithm Based on Wavelet Coefficient Blocks for Robust Image Transmission over Noisy Channel

Xiu

Zhu

2010

2010 Third International Symposium on Information Science and Engineering

View full text Add to dashboard Cite

A modified set partitioning in hierarchical trees (SPIHT) algorithm based on wavelet coefficient blocks, which is more appropriate to robust image transmission over noisy channels when using unequal error protection (UEP) scheme, is presented. According to the algorithm characteristic of traditional SPIHT coding that scanning once can process four wavelet coefficients, our approach divides the wavelet transform coefficient matrix into corresponding coefficient blocks and encodes every block with different strategies. When the maximum value of the coefficient block is less than the current threshold, only one bit is used for coding, thus to reduce the loss of coding efficiency caused by repeat coding and early coding for non-important coefficients. Simulation results show that the proposed algorithm combined with UEP can provide a better peak signal to noise ratio (PSNR) performance for image transmission over noisy channel, especially at low bit rate.

show abstract