UPPER BOUND ON SNR GAIN IN WAVELET TRANSFORM PREDICTIVE-ENTROPY IMAGE CODING

1998 ◽  
Vol 08 (02) ◽  
pp. 267-272
Author(s):  
DRAGORAD MILOVANOVIĆ ◽  
ZORAN BOJKOVIĆ ◽  
ANDREJA SAMČOVIĆ
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Upper Bound ◽  
Rate Distortion ◽  
Upper Bounds ◽  
Discrete Wavelet ◽  
Theory Approach ◽  
Still Image ◽  
Rate Distortion Theory ◽  
Snr Gain

Using the rate-distortion theory approach and assuming the Laplacian probability density function of the quantizer input signal, we propose a function which gives the signal-to-quantizing-noise ratio (SNR) gain in discrete wavelet transform predictive-entropy coding over fullband predictive image coding. The upper bound on SNR gain is determined as a function of the subband number only. The practical SNR performances of realizable wavelet still image coders are compared with their theoretical bounds. The computer simulation results of wavelet based predictive coded test images show that the SNR gain grows faster with the subband number increment than its theoretical upper bounds do.

Study of the Discrete Wavelet Transform Based on JPEG2000 Image Compression Algorithm

2013 ◽  
Vol 464 ◽  
pp. 411-415
Author(s):  
Jin Cai ◽  
Shuo Wang
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Image Coding ◽  
Dynamic Range ◽  
Compression Algorithm ◽  
Discrete Wavelet ◽  
Coding System ◽  
Jpeg 2000 ◽  
Image Compression Algorithm

JPEG 2000 is a new image coding system that uses state-of-the-art compression techniques based on wavelet technology. As interactive multimedia technologies evolve, the requirements for the file format used to store the image data continue to evolve. The size and bit depth collected for an image to increase the resolution and extend the dynamic range and color gamut. Discrete Wavelet transform based embedded image coding method is the basis of JPEG2000. Image compression algorithm for the proper use and display of the image is a requirement for digital photography.

Computational Paradigm and Quantitative Optimization to Parallel Processing Performance of Still Image Compression

2017 ◽  
Vol 2 (4) ◽  
pp. 11-17
Author(s):  
P. S. Jagadeesh Kumar ◽  
Tracy Lin Huan ◽  
Yang Yung
Keyword(s):  
Wavelet Transform ◽  
Parallel Processing ◽  
Image Compression ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Processing Unit ◽  
Digital Information ◽  
Complex Wavelet Transform ◽  
Still Image ◽  
Central Processing

Fashionable and staggering evolution in inferring the parallel processing routine coupled with the necessity to amass and distribute huge magnitude of digital records especially still images has fetched an amount of confronts for researchers and other stakeholders. These disputes exorbitantly outlay and maneuvers the digital information among others, subsists the spotlight of the research civilization in topical days and encompasses the lead to the exploration of image compression methods that can accomplish exceptional outcomes. One of those practices is the parallel processing of a diversity of compression techniques, which facilitates split, an image into ingredients of reverse occurrences and has the benefit of great compression. This manuscript scrutinizes the computational intricacy and the quantitative optimization of diverse still image compression tactics and additional accede to the recital of parallel processing. The computational efficacy is analyzed and estimated with respect to the Central Processing Unit (CPU) as well as Graphical Processing Unit (GPU). The PSNR (Peak Signal to Noise Ratio) is exercised to guesstimate image re-enactment and eminence in harmonization. The moments are obtained and conferred with support on different still image compression algorithms such as Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DTCWT), Set Partitioning in Hierarchical Trees (SPIHT), Embedded Zero-tree Wavelet (EZW). The evaluation is conceded in provisos of coding efficacy, memory constraints, image quantity and quality.

Digital Image Watermarking Based on Fractal Image Coding

2016 ◽  
pp. 388-399 ◽  
Author(s):  
Channapragada R. S. G. Rao ◽  
Munaga V. N. K. Prasad
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Image Watermarking ◽  
Low Frequency ◽  
Time Signal ◽  
Discrete Wavelet ◽  
Mathematical Function ◽  
Ridgelet Transform ◽  
Fractal Image ◽  
Line Singularities

This chapter proposes a watermarking technique using Ridgelet and Discrete Wavelet Transform (DWT) techniques. A wavelet transform is the wavelet function representation. A wavelet is a mathematical function which divides a continuous time signal into different scale components, where each scale components is assigned with a frequency range. Wavelets represent objects with point singularities, while ridgelets represents objects with line singularities. The Ridgelet transform Technique is a multi-scale representation for functions on continuous spaces that are smooth away from discontinuities along lines. The proposed technique applies Ridgelet transform on the cover image to obtain ridgelet coefficients. These coefficients are transformed by using 2-level DWT to get low frequency sub-bands – LL1 and LL2. The mutual similarities between LL1 and LL2 sub-bands are considered for embedding watermark. The obtained watermarked image has better quality when compared to a few exiting methods.

Image Coding Using Dual-Tree Discrete Wavelet Transform

2008 ◽  
Vol 17 (9) ◽  
pp. 1555-1569 ◽  
Author(s):  
Jingyu Yang ◽  
Yao Wang ◽  
Wenli Xu ◽  
Qionghai Dai
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Coding ◽  
Discrete Wavelet

Subband Coding of Signals

2002 ◽  
pp. 174-199
Author(s):  
Murilo B. de Carvalho ◽  
Eduardo A.B. da Silva
Keyword(s):  
Video Coding ◽  
Rate Distortion ◽  
Subband Coding ◽  
Wavelet Packets ◽  
Audio Compression ◽  
Still Image ◽  
Rate Distortion Theory

In this chapter we deal with subband coding of signals. We begin with an introduction to rate-distortion theory, and after that we discuss subband compression schemes in general. We then describe popular still image and video coding algorithms using subband decompositions, including DCT and wavelet-based methods. R-D optimization-based techniques such as wavelet packets are also described. We finish the chapter with a brief account of subband-based audio compression schemes.

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