Laplacian and generalized Gaussian data arise in the transform and subband coding of images. This paper describes a method of rotating independent, identically distributed (i.i.d.) Laplacian-like data in multiple dimensions to significantly improve the overload characteristics for quantization. The rotation is motivated by the geometry of the Laplacian probability distribution, and can be achieved with only additions and subtractions using a Walsh-Hadamard transform. Its theoretical and simulated results for scalar, lattice, and polar quantization are presented in this paper, followed by a direct application to image compression. We show that rotating the image data before quantization not only improves compression performance, but also increases robustness to the channel noise and deep fades often encountered in wireless communication.

Pyramid vector quantization (PVQ) uses the lattice points of a pyramidal shape in multidimensional space as the quantizer codebook. It is a fixed-rate quantization technique that can be used for the compression of Laplacian-like sources arising from transform and subband image coding, where its performance approaches the optimal entropy-coded scalar quantizer without the necessity of variable length codes. In this paper, we investigate the use of PVQ for compressed image transmission over noisy channels, where the fixed-rate quantization reduces the susceptibility to bit-error corruption. We propose a new method of deriving the indices of the lattice points of the multidimensional pyramid and describe how these techniques can also improve the channel noise immunity of general symmetric lattice quantizers. Our new indexing scheme improves channel robustness by up to 3 dB over previous indexing methods, and can be performed with similar computational cost. The final fixed-rate coding algorithm surpasses the performance of typical Joint Photographic Experts Group (JPEG) implementations and exhibits much greater error resilience.

Our present ability to work with video has been confined to a wired environment, requiring both the video encoder and decoder to be physically connected to a power supply and a wired communication link. This paper describes an integrated approach to the design of a portable video-on-demand system capable of delivering high-quality image and video data in a wireless communication environment. The discussion will focus on both the algorithm and circuit design techniques developed for implementing a low-power video compression/decompression system at power levels that are two orders of magnitude below existing solutions. This low-power video compression system not only provides a compression efficiency similar to industry standards, but also maintains a high degree of error tolerance to guard against transmission errors often encountered in wireless communication. The required power reduction can best be attained through reformulating compression algorithms for energy conservation. We developed an intra-frame compression algorithm that requires minimal computation energy in its hardware implementations.>

Laplacian and generalized Gaussian data arise in image and speech coding. Simple rotations of independent, identically distributed Laplacian and generalized Gaussian data in multiple dimensions can improve the granular and overload characteristics for quantization. In this paper, we describe the practical properties of multidimensional rotations for both scalar and lattice quantization and then apply them to image compression over noisy channels.>

The robustness of image and video compression in the presence of time-varying channel error has received increased interest with the emergence of portable digital receivers and computers. To achieve robust compression, pyramid vector quantization (PVQ) can be used. It is a fixed-rate quantization scheme suited to Laplacian-like sources, such as those arising from transform and subband image coding. The authors propose a new method of PVQ which not only improves upon the image compression performance of typical JPEG implementations, but also demonstrates excellent resilience to channel error.>

This paper describes two method to improve the robustness of transmitting such data over wireless communication channels: adaptively changing the quantizer codebook at the decoder, and optimizing codebook design for bursty errors.>

Motion estimation is one of the most computationally intensive tasks required in digital video compression. The authors propose parallelizable motion estimation algorithms with low computational cost for both sub-optimal and optimal motion estimation. For efficient optimal motion estimation, they develop theoretical bounds based on convexity to reduce the required operations. All algorithms are tested on multiple motion estimation paradigms for performance evaluation.>

As evidenced by the theoretical modeling and simulation presented, the optimal quantizer solution for run-length and Huffman coding is quite addressable for the Laplacian case. The step sizes may be calculated in advance for images that share common statistics. Furthermore, the model can also be used for other uses such as bit-management for rate control and lower bound estimation for compressed file sizes; it is also the first step in the general solution to the transform coder quantization problem.>