Glen P. Abousleman

Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB. >

A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling

A no-reference perceptual sharpness quality metric, inspired by visual attention information, is presented for a better simulation of the Human Visual System (HVS) response to blur distortions. Saliency information about a scene is used to accentuate blur distortions around edges present in conspicuous areas and attenuate those distortions present in the rest of the image. Simulation results are presented to illustrate the performance of the proposed metric.

Hyperspectral image compression using entropy-constrained predictive trellis coded quantization

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands.

Image coding with robust channel-optimized trellis-coded quantization

This paper presents a wavelet-based image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. A phase scrambling stage is also used to further increase the coding performance and robustness to nonstationary signals and channels. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder with no explicit channel coding for error protection. For the considered TCQ trellis structure, a general expression is derived for the transition probability matrix. In terms of the TCQ encoding rat and the channel bit error rate, and is used to design the COTCQ stage of the image coder. The robust nature of the coder also increases the security level of the encoded bit stream and provides a much more visually pleasing rendition of the decoded image. Examples are presented to illustrate the performance of the proposed robust image coder.

Orthogonal Rotation-Invariant Moments for Digital Image Processing

Orthogonal rotation-invariant moments (ORIMs), such as Zernike moments, are introduced and defined on a continuous unit disk and have been proven powerful tools in optics applications. These moments have also been digitized for applications in digital image processing. Unfortunately, digitization compromises the orthogonality of the moments and, therefore, digital ORIMs are incapable of representing subtle details in images and cannot accurately reconstruct images. Typical approaches to alleviate the digitization artifact can be divided into two categories: (1) careful selection of a set of pixels as close approximation to the unit disk and using numerical integration to determine the ORIM values, and (2) representing pixels using circular shapes such that they resemble that of the unit disk and then calculating ORIMs in polar space. These improvements still fall short of preserving the orthogonality of the ORIMs. In this paper, in contrast to the previous methods, we propose a different approach of using numerical optimization techniques to improve the orthogonality. We prove that with the improved orthogonality, image reconstruction becomes more accurate. Our simulation results also show that the optimized digital ORIMs can accurately reconstruct images and can represent subtle image details.

Adaptive wavelet coding of hyperspectral imagery

A system is presented for compression of hyperspectral imagery. Specifically, DPCM is used for spectral decorrelation, while an adaptive 2-D discrete wavelet coding scheme is used for spatial decorrelation. Trellis coded quantization is used to encode the wavelet coefficients. Side information and rate allocation strategies are discussed. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This entropy constrained system achieves a compression ratio of greater than 70:1 with an average PSNR of the coded hyperspectral sequence exceeding 41 dB.

Wavelet-based hyperspectral image coding using robust fixed-rate trellis-coded quantization

A system is presented for compression of hyperspectral imagery. Specifically, DPCM is used for spectral decorrelation, while a robust 2-D discrete wavelet coding scheme is used for spatial decorrelation. Trellis-coded quantization is used to encode the wavelet coefficients. Side information and rate allocation strategies are discussed. Fixed-rate codebooks are designed using a modified version of the generalized Lloyd algorithm. This system achieves a compression ratio of greater than 70:1, with an average PSNR of the coded hyperspectral sequence exceeding 40 dB.

Knowledge-based hierarchical region-of-interest detection

Detecting regions of interest (ROIs) in a complex image is a critical step in many image processing applications. In this paper, we present a new algorithm that addresses several challenges in ROI detection. The novelty of our algorithm includes: (i) every ROI contains one and only one object; (ii) the detected ROIs can have irregular shapes as opposed to the rectangular shapes that are typical of other algorithms; (iii) the algorithm is applicable to images that contain connected objects, or when the objects are broken into pieces; (iv) the algorithm is not sensitive to contrast levels in the image, and is robust to noise. These characteristics make the proposed algorithm applicable to low-resolution, real-world imagery without costly post-processing. The proposed algorithm is shown to provide outstanding performance with low-quality imagery, and is shown to be fast and robust.

Robust hyperspectral image coding with channel-optimized trellis-coded quantization

This paper presents a wavelet-based hyperspectral image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder and does not include any channel coding for error protection. The robust nature of the coder increases the security level of the encoded bit stream, and provides a much higher quality decoded image. In the absence of channel noise, the proposed coder is shown to achieve a compression ratio greater than 70:1, with an average peak SNR of the coded hyperspectral sequence exceeding 40 dB. Additionally, the coder is shown to exhibit graceful degradation with increasing channel errors.

Enhancement and compression techniques for hyperspectral data

The next generation of satellite-borne sensors will combine high spatial resolution with fine spectral resolution. A typical data set for a single frame of imagery may contain a few hundred images occupying many gigabytes of space. Clearly, traditional image processing algorithms cannot be directly applied to such a vast quantity of data. We investigate enhancement and compression algorithms that use the spectral correlation present in high-resolution imagery to reduce the computational complexity of processing the imagery. The algorithm employs a principal component transformation to reduce the size of the data set. Enhancing the reduced set of images provides equivalent results to processing each of the original images with far fewer computations. The compression algorithm utilizes a hybrid discrete cosine transform-differential pulse code modulation (DCT-DPCM) transform. The DCT is computed for each image, a bit map is generated for the DCT coefficients, and DPCM is used to encode the coefficients across the bands. Compression at less than 0.5 bits/pixel with negligible visual degradation is obtained.