Multiple Description Quantization via Gram-Schmidt Orthogonalization

The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achievable MD rate-distortion region, and show that any point in the EGC region can be achieved via a successive quantization scheme along with quantization splitting. For the quadratic Gaussian case, the proposed scheme has an intrinsic connection with the Gram-Schmidt orthogonalization, which implies that the whole Gaussian MD rate-distortion region is achievable with a sequential dithered lattice-based quantization scheme as the dimension of the (optimal) lattice quantizers becomes large. Moreover, this scheme is shown to be universal for all i.i.d. smooth sources with performance no worse than that for an i.i.d. Gaussian source with the same variance and asymptotically optimal at high resolution. A class of low-complexity MD scalar quantizers in the proposed general framework also is constructed and is illustrated geometrically; the performance is analyzed in the high resolution regime, which exhibits a noticeable improvement over the existing MD scalar quantization schemes.Comment: 48 pages; submitted to IEEE Transactions on Information Theor

Image Utility Assessment and a Relationship with Image Quality Assessment

International audiencePresent quality assessment (QA) algorithms aim to generate scores for natural images consistent with subjective scores for the quality assessment task. For the quality assessment task, human observers evaluate a natural image based on its perceptual resemblance to a reference. Natural images communicate useful information to humans, and this paper investigates the utility assessment task, where human observers evaluate the usefulness of a natural image as a surrogate for a reference. Current QA algorithms implicitly assess utility insofar as an image that exhibits strong perceptual resemblance to a reference is also of high utility. However, a perceived quality score is not a proxy for a perceived utility score: a decrease in perceived quality may not affect the perceived utility. Two experiments are conducted to investigate the relationship between the quality assessment and utility assessment tasks. The results from these experiments provide evidence that any algorithm optimized to predict perceived quality scores cannot immediately predict perceived utility scores. Several QA algorithms are evaluated in terms of their ability to predict subjective scores for the quality and utility assessment tasks. Among the QA algorithms evaluated, the visual information fidelity (VIF) criterion, which is frequently reported to provide the highest correlation with perceived quality, predicted both perceived quality and utility scores reasonably. The consistent performance of VIF for both the tasks raised suspicions in light of the evidence from the psychophysical experiments. A thorough analysis of VIF revealed that it artificially emphasizes evaluations at finer image scales (i.e., higher spatial frequencies) over those at coarser image scales (i.e., lower spatial frequencies). A modified implementation of VIF, denoted VIF*, is presented that provides statistically significant improvement over VIF for the quality assessment task and statistically worse performance for the utility assessment task. A novel utility assessment algorithm, referred to as the natural image contour evaluation (NICE), is introduced that conducts a comparison of the contours of a test image to those of a reference image across multiple image scales to score the test image. NICE demonstrates a viable departure from traditional QA algorithms that incorporate energy-based approaches and is capable of predicting perceived utility scores

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

The first IEEE workshop on the Future of Research Curation and Research Reproducibility

This report describes perspectives from the Workshop on the Future of Research Curation and Research Reproducibility that was collaboratively sponsored by the U.S. National Science Foundation (NSF) and IEEE (Institute of Electrical and Electronics Engineers) in November 2016. The workshop brought together stakeholders including researchers, funders, and notably, leading science, technology, engineering, and mathematics (STEM) publishers. The overarching objective was a deep dive into new kinds of research products and how the costs of creation and curation of these products can be sustainably borne by the agencies, publishers, and researcher communities that were represented by workshop participants.National Science Foundation Award #164101

Robust Image Transmission using Resynchronizing Variable-Length Codes and Error Concealment

Resynchronizing variable-length codes (RVLCs) for large alphabets are designed by first creating resynchronizing Huffman codes and then adding an extended synchronizing codeword, and the RVLCs are applied to both JPEG and wavelet-based image compression. The RVLCs demonstrate the desired resynchronization properties, both at a symbol level and structurally so that decoded data can be correctly placed within an image following errors. The encoded images, when subject to both structural and statistical error detection and concealment, can tolerate BERs of up to and are very tolerant of burst errors. The RVLC-JPEG images have negligible overhead at visually lossless bit rates, while the RVLC-wavelet overhead can be adjusted based on the desired tolerance to burst errors and typically ranges from 7-18%. The tolerance to both bit and burst errors demonstrates that images coded with such RVLCs can be transmitted over imperfect channels suffering bit errors or packet losses without channel co..

System-level Issues for A Multi-Rate Video VOD System

This document describes system-level issues for incorporation of multi-rate video into a videoon-demand system. The issues are broadly separated into two classes: rate negotiation, involving selection of a rate at which the requested video will be provided, and rate delivery, involving delivery of the coded stream to the user. Requirements of the server, the network, and the set-top box are developed, and are shown to be interrelated for design of an efficient system. Additionally, constraints are imposed on the video coding algorithm. Specific research topics for further investigation are proposed. Current video-on-demand (von) systems in the trial phase are homogeneous- all set-tops are identical, the video is delivered to each set-top via the same physical medium, and each video or program is only coded at one rate. However, heterogeneity exists in the viewing device, as there are many sizes and qualities of televisions, and even computers with NTSC cards can displa

Reconstruction-Optimized Lapped Orthogonal Transforms

Packetized image communication over lossy channels presents a reconstruction problem at the decoder. To date, reconstruction algorithms have been developed for fixed coding techniques. This paper examines the dual problem --- a block-based coding technique, namely a family of lapped orthogonal transforms (LOTs), is designed to maximize the reconstruction performance of a fixed reconstruction algorithm. Mean-reconstruction, in which missing coefficient blocks are replaced with averages of their available neighbors, is selected for simplicity and implementation ease. A reconstruction criterion is defined in terms of mean-squared error, and is used to define the reconstruction gain. A family of LOTs is then designed to consider both reconstruction gain and coding gain. Reconstruction capability and compression are exchanged, and the LOT family consists of transforms that provide increasing reconstruction capability with lower coding gain. A transform can therefore be selected based on ch..