Visually Lossless Perceptual Image Coding Based on Natural- Scene Masking Models

Perceptual coding is a subdiscipline of image and video coding that uses models of human visual perception to achieve improved compression efficiency. Nearly, all image and video coders have included some perceptual coding strategies, most notably visual masking. Today, modern coders capitalize on various basic forms of masking such as the fact that distortion is harder to see in very dark and very bright regions, in regions with higher frequency content, and in temporal regions with abrupt changes. However, beyond these obvious forms of masking, there are many other masking phenomena that occur (and co-occur) when viewing natural imagery. In this chapter, we present our latest research in perceptual image coding using natural-scene masking models. We specifically discuss: (1) how to predict local distortion visibility using improved natural-scene masking models and (2) how to apply the models to high efficiency video coding (HEVC). As we will demonstrate, these techniques can offer 10–20% fewer bits than baseline HEVC in the ultra-high-quality regime

Patch-based structural masking model with an application to compression

The ability of an image region to hide or mask a given target signal continues to play a key role in the design of numerous image processing and vision systems. However, current state-of-the-art models of visual masking have been optimized for artificial targets placed upon unnatural backgrounds. In this paper, we (1) measure the ability of natural-image patches in masking distortion; (2) analyze the performance of a widely accepted standard masking model in predicting these data; and (3) report optimal model parameters for different patch types (textures, structures, and edges). Our results reveal that the standard model of masking does not generalize across image type; rather, a proper model should be coupled with a classification scheme which can adapt the model parameters based on the type of content contained in local image patches. The utility of this adaptive approach is demonstrated via a spatially adaptive compression algorithm which employs patch-based classification. Despite the addition of extra side information and the high degree of spatial adaptivity, this approach yields an efficient wavelet compression strategy that can be combined with very accurate rate-control procedures.Peer reviewedElectrical and Computer Engineerin

A Position Effect on the Heritability of Epigenetic Silencing

In animals and yeast, position effects have been well documented. In animals, the best example of this process is Position Effect Variegation (PEV) in Drosophila melanogaster. In PEV, when genes are moved into close proximity to constitutive heterochromatin, their expression can become unstable, resulting in variegated patches of gene expression. This process is regulated by a variety of proteins implicated in both chromatin remodeling and RNAi-based silencing. A similar phenomenon is observed when transgenes are inserted into heterochromatic regions in fission yeast. In contrast, there are few examples of position effects in plants, and there are no documented examples in either plants or animals for positions that are associated with the reversal of previously established silenced states. MuDR transposons in maize can be heritably silenced by a naturally occurring rearranged version of MuDR. This element, Muk, produces a long hairpin RNA molecule that can trigger DNA methylation and heritable silencing of one or many MuDR elements. In most cases, MuDR elements remain inactive even after Muk segregates away. Thus, Muk-induced silencing involves a directed and heritable change in gene activity in the absence of changes in DNA sequence. Using classical genetic analysis, we have identified an exceptional position at which MuDR element silencing is unstable. Muk effectively silences the MuDR element at this position. However, after Muk is segregated away, element activity is restored. This restoration is accompanied by a reversal of DNA methylation. To our knowledge, this is the first documented example of a position effect that is associated with the reversal of epigenetic silencing. This observation suggests that there are cis-acting sequences that alter the propensity of an epigenetically silenced gene to remain inactive. This raises the interesting possibility that an important feature of local chromatin environments may be the capacity to erase previously established epigenetic marks

DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability

For DNA barcoding to succeed as a scientific endeavor an accurate and expeditious query sequence identification method is needed. Although a global multiple–sequence alignment can be generated for some barcoding markers (e.g. COI, rbcL), not all barcoding markers are as structurally conserved (e.g. matK). Thus, algorithms that depend on global multiple–sequence alignments are not universally applicable. Some sequence identification methods that use local pairwise alignments (e.g. BLAST) are unable to accurately differentiate between highly similar sequences and are not designed to cope with hierarchic phylogenetic relationships or within taxon variability. Here, I present a novel alignment–free sequence identification algorithm–BRONX–that accounts for observed within taxon variability and hierarchic relationships among taxa. BRONX identifies short variable segments and corresponding invariant flanking regions in reference sequences. These flanking regions are used to score variable regions in the query sequence without the production of a global multiple–sequence alignment. By incorporating observed within taxon variability into the scoring procedure, misidentifications arising from shared alleles/haplotypes are minimized. An explicit treatment of more inclusive terminals allows for separate identifications to be made for each taxonomic level and/or for user–defined terminals. BRONX performs better than all other methods when there is imperfect overlap between query and reference sequences (e.g. mini–barcode queries against a full–length barcode database). BRONX consistently produced better identifications at the genus–level for all query types