Alexander I. Drukarev

Simplified segmentation for compound image compression

There are three basic segmentation schemes for compound image compression: object-based, layer-based, and block-based. This paper discusses the relative advantages of each scheme and architecture, and studies the use of fast classification techniques for a segmentation that can be used together with a chosen compression architecture. Particularly, we consider classification techniques working on approximate object boundaries, which reaches the localization and precision of the segmentation, but in exchange allows faster, one-pass segmentation, low memory requirements, and segmentation map that is better matched to existing compression methods. We show numerical results obtained on a printer application environment, where rigorous standards of visual quality have to be satisfied.

Performance of Error-Correcting Codes on Channels with Memory

Most magnetic recording and many other digital communication channels exhibit statistical dependencies among errors. The design of error-control schemes for such channels .requires proper models and tools that can be used to evaluate error performance after decoding. In this paper a simplified partitioned model of a channel with memory suggested by B. D. Fritchman is considered. This model is used to derive expressions for block and bit error probability bounds for major block burst-error-correcting codes: interleaved, single burst-correcting, and nonbiuary codes. The model-based bounds are compared to the experimental ones using the data obtained for helical scan magnetic tape recorders. The comparison showed an agreement between experimental and analytical data within one order of magnitude, with the average difference being as small as 16 percent in some cases. A simple and effective implementation of a multiple burst-error-correcting scheme based on the majority-logic decoding of interleaved binary codes is suggested. The scheme requires about ten off-the-shelf ICs for both encoder and decoder with the interleaving degree up to 512.

SBHP-a low complexity wavelet coder

We present a low-complexity entropy coder originally designed to work in the JPEG2000 image compression standard framework. The algorithm is meant for embedded and non-embedded coding of wavelet coefficients inside a subband, and is called subband-block hierarchical partitioning (SBHP). It was extensively tested following the standard experiment procedures, and it was shown to yield a significant reduction in the complexity of entropy coding, with small loss in compression performance. Furthermore, it is able to seamlessly support all JPEG2000 features. We present a description of the algorithm, an analysis of its complexity, and a summary of the results obtained after its integration into the verification model (VM).

Compression-related properties of color spaces

In modern color imaging a variety of color spaces are used to represent an image. It has been well established in literature that the choice of color space has an impact on the achievable compression. The purpose of this research is to investigate compression related properties of various color spaces. We investigated what we considered to be the most popular color spaces used today, namely, RGB, YIQ, YUV, YCbCr, HSI, CMYK, XYZ and CIELab. The following properties of color spaces that influence compressibility have been studied: energy distribution among color planes, plane bandwidth, DCT energy compaction, and impact of gamma correction. We also compared the compressibility of low resolution images, which have been used for most of the compression results reported in literature, with high resolution images, which are becoming increasingly important for modern imaging applications. The findings of this research have been illustrated by comparing the actual JPEG compression results for YCbCr and CIELab spaces.

Noise Performance and SNR Threshold in PFM

Pulse frequency modulation (PFM) has been considered by several authors for transmitting wide-band signals. However, the noise performance of PFM has been analyzed only above the SNR threshold. In this correspondence the results on the noise performance of PFM available in the literature are summarized, and a more general expression describing noise performance of PFM, both above and below the SNR threshold, is derived. This expression can be used to evaluate the SNR threshold for channels with both signal-independent noise (e.g., metallic facilities) and signal-dependent noise (e.g., fiber optic facilities). In particular, it is shown that in systems with signal-independent white noise, the SNR threshold is approximately 20 dB.