Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties

Conditional weighted universal source codes: second order statistics in universal coding

We consider the use of second order statistics in two-stage universal source coding. Examples of two-stage universal codes include the weighted universal vector quantization (WUVQ), weighted universal bit allocation (WUBA), and weighted universal transform coding (WUTC) algorithms. The second order statistics are incorporated in two-stage universal source codes in a manner analogous to the method by which second order statistics are incorporated in entropy constrained vector quantization (ECVQ) to yield conditional ECVQ (CECVQ). In this paper, we describe an optimal two-stage conditional entropy constrained universal source code along with its associated optimal design algorithm and a fast (but nonoptimal) variation of the original code. The design technique and coding algorithm here presented result in a new family of conditional entropy constrained universal codes including but not limited to the conditional entropy constrained WUVQ (CWUVQ), the conditional entropy constrained WUBA (CWUBA), and the conditional entropy constrained WUTC (CWUTC). The fast variation of the conditional entropy constrained universal codes allows the designer to trade off performance gains against storage and delay costs. We demonstrate the performance of the proposed codes on a collection of medical brain scans. On the given data set, the CWUVQ achieves up to 7.5 dB performance improvement over variable-rate WUVQ and up to 12 dB performance improvement over ECVQ. On the same data set, the fast variation of the CWUVQ achieves identical performance to that achieved by the original code at all but the lowest rates (less than 0.125 bits per pixel)

A novel steganography approach for audio files

We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform (DCT) and high frequency minimization encoding algorithm. The method involves compression-encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR)

A survey of parallel algorithms for fractal image compression

This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm