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Transmitting video over wireless is a challenging problem since video may be seriously distorted due to packet errors caused by wireless channels. The capability of predicting transmission distortion (i.e., video distortion caused by packet errors) can assist in designing video encoding and transmission schemes that achieve maximum video quality or minimum end-to-end video distortion. This paper is aimed at deriving formulas for predicting transmission distortion. The contribution of this paper is twofold. First, we identify the governing law that describes how the transmission distortion process evolves over time and analytically derive the transmission distortion formula as a closed-form function of video frame statistics, channel error statistics, and system parameters. Second, we identify, for the first time, two important properties of transmission distortion. The first property is that the clipping noise, which is produced by nonlinear clipping, causes decay of propagated error. The second property is that the correlation between motion-vector concealment error and propagated error is negative and has dominant impact on transmission distortion, compared with other correlations. Due to these two properties and elegant error/distortion decomposition, our formula provides not only more accurate prediction but also lower complexity than the existing methods. [ABSTRACT FROM AUTHOR]

The \ell\infty-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding, while still imposing a tight error bound at each pixel. However, this technique becomes inferior in the \ell2 distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the \ell2 distortion of \ell\infty-decoded images and retain the advantages of both minmax and least-square approximations. The soft decoding is performed in a framework of image restoration that exploits the tight error bounds afforded by the \ell\infty-constrained coding and employs a context modeler of quantization errors. Experimental results demonstrate that the \ell\infty-constrained hard decoded images can be restored to gain more than 2 dB in peak signal-to-noise ratio PSNR, while still retaining tight error bounds on every single pixel. The new soft decoding technique can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1 bpp, a critical rate region for applications of near-lossless image compression. All the coding gains are made without increasing the encoder complexity as the heavy computations to gain coding efficiency are delegated to the decoder. [ABSTRACT FROM AUTHOR]

Multitoning is the representation of digital pictures using a given set of available color intensities, which are also known as tones or quantization levels. It can be viewed as the generalization of halftoning, where only two such quantization levels are available. Its main application is for printing and, similar to halftoning, can be applied to both colored and grayscale images. In this paper, we present a method to produce multitones based on the multiscale error diffusion technique. Key characteristics of this technique are: 1) the use of an image quadtree; 2) the quantization order of the pixels being determined through “maximum intensity guidance” on the image quadtree; and 3) noncausal error diffusion. Special care has been given to the problem of banding, which is one of the inherent limitations in error diffusion when applied to multitoning. Banding is evident in areas of the image with values close to one of the available quantization levels; our approach is to apply a preprocessing step to alleviate part of the problem. Our results are evaluated both in terms of visual appearance and using a set of standard metrics, with the latter demonstrating the blue-noise characteristics and very low anisotropy of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we present an analysis of the dyadic approximation error introduced by the integerization of transform coding in H.264/AVC-like codecs. We derive the analytical formulations for dyadic approximation error and nonorthogonality error. We further classify the dyadic approximation error into a “system error” and a “nonflat error,” and proposed two models for them. We found that the “nonflat error” has a substantial impact on video quality if the number of shifting bits at decoder side (DQ_BITS) is small. We also give a theoretical justification on why scaling factors at encoder side are better to be adapted to the rescaling factors at decoder side in H.264/AVC-like codecs. [ABSTRACT FROM AUTHOR]

A spectral agreement between the original scene and a printed reproduction is required to achieve an illuminant-invariant visual match. This is usually impossible since the spectral gamut of typical printing systems is only a small subset of all natural reflectances. Out-of gamut reflectances need to be mapped into the spectral gamut of the printer minimizing the perceived error between original and reproduction for more than one illuminant. In this paper, we propose an algorithmic framework for spectral gamut mapping to achieve a reproduction that is as visually correct as a colorimetric reproduction for one illuminant and is superior for a set of other illuminants. A sequence of hierarchical mappings in 3-D color spaces are performed utilizing the observer's color quantization to increase the spectral variability of subsequent transformations: For the most important illuminant a traditional colorimetric gamut mapping is performed. For any additional illuminants colors are mapped onto pixel-dependent paramer mismatch gamuts preserving the visual equivalence of previous transformations. We present a separation method for investigating the spectral gamut mapping framework and show that hue shifts and chroma gains cannot be always avoided for the second and subsequent illuminants and that the order of illuminants has a large impact on the final reproduction. [ABSTRACT FROM AUTHOR]

In this paper, we are to solve the drift problem of multiple description video coding on packet loss channels by using state-of-the-art distributed techniques. We first present an asymptotically optimal code design of multiple descriptions in the Wyner–Ziv (MDWZ) setting. Then we propose a distributed multiple description video coding (DMDVC) scheme, which performs MDWZ coding on each nonintra coded frame. Instead of the prediction loops used in traditional multiple description video coding, Slepian–Wolf based coding is used to exploit interframe correlations. A bitplane extraction scheme is proposed to improve the balance between two descriptions, so that side informations can be interchanged between the side decoders of DMDVC with negligible quality degradation, which is crucial to robust transmission over packet loss channels. Experiment results demonstrate the robustness of our scheme, especially at high packet loss rates. [ABSTRACT FROM AUTHOR]

In this paper, a novel arrangement for quantizer levels in the Quantization Index Modulation (QIM) method is proposed. Due to perceptual advantages of logarithmic quantization, and in order to solve the problems of a previous logarithmic quantization-based method, we used the compression function of µ-Law standard for quantization. In this regard, the host signal is first transformed into the logarithmic domain using the µ-Law compression function. Then, the transformed data is quantized uniformly and the result is transformed back to the original domain using the inverse function. The scalar method is then extended to vector quantization. For this, the magnitude of each host vector is quantized on the surface of hyperspheres which follow logarithmic radii. Optimum parameter µ for both scalar and vector cases is calculated according to the host signal distribution. Moreover, inclusion of a secret key in the proposed method, similar to the dither modulation in QIM, is introduced. Performance of the proposed method in both cases is analyzed and the analytical derivations are verified through extensive simulations on artificial signals. The method is also simulated on real images and its performance is compared with previous scalar and vector quantization-based methods. Results show that this method features stronger a watermark in comparison with conventional QIM and, as a result, has better performance while it does not suffer from the drawbacks of a previously proposed logarithmic quantization algorithm. [ABSTRACT FROM AUTHOR]

Compound images are a combination of text, graphics and natural image. They present strong anisotropic features, especially on the text and graphics parts. These anisotropic features often render conventional compression inefficient. Thus, this paper proposes a novel coding scheme from the H.264 intraframe coding. In the scheme, two new intramodes are developed to better exploit spatial correlation in compound images. The first is the residual scalar quantization (RSQ) mode, where intrapredicted residues are directly quantized and coded without transform. The second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. In this mode, an image block is represented by several representative colors, referred to as base colors, and an index map to compress. Every block selects its coding mode from two new modes and the previous intramodes in H.264 by rate-distortion optimization (RDO). Experimental results show that the proposed scheme improves the coding efficiency even more than 10 dB at most bit rates for compound images and keeps a comparable efficient performance to H.264 for natural images. [ABSTRACT FROM AUTHOR]

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high-rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM index is then used to derive bounds on the average SSIM index as a function of quantization rate for uniform, Gaussian, and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications. [ABSTRACT FROM AUTHOR]

Three-dimensional graphic scenes contain various mesh objects in one geometric space where different objects have potentially unequal importance regarding display. This paper proposes an object-oriented system for efficiently coding and streaming 3-D scene databases in lossy and rate-constrained environments. Vector quantization (VQ) is exploited to code 3-D scene databases into multiresolution hierarchies. For the best distortion-rate performance, adaptive quantization precisions are allocated to different objects and different layers of each object based on a weighted distortion model. Upon transmission, scalably coded objects are delivered in respective packet sequences to preserve their manipulation independency. For packet loss resilience, a plurality of FEC codes are generated as "parity objects" parallel to graphic objects, which protect the graphic objects concurrently and also preferentially in regard to their unequal decoding importance. A rate-distortion optimization framework is then developed, which performs rate allocation between graphic objects and parity objects and generates the parity data properly. We show that, by treating graphic objects jointly and preferentially in source and channel coding while preserving their independencies in transport, the proposed system reduces the receiving distortion of the 3-D database significantly compared to conventional methods. [ABSTRACT FROM AUTHOR]

Transform-coded images exhibit distortions that fall outside of the assumptions of traditional denoising techniques. In this paper, we use tools from robust signal processing to construct linear, worst-case estimators for the denoising of transform compressed images. We show that while standard denoising is fundamentally determined by statistical models for images alone, the distortions induced by transform coding are heavily dependent on the structure of the transform used. Our method, thus, uses simple models for the image and for the quantization error, with the latter capturing the transform dependency. Based on these models, we derive optimal, linear estimators of the original image that are optimal in the mean-squared error sense for the worst-case cross correlation between the original and the quantization error. Our construction is transform agnostic and is applicable to transforms from block discrete cosine transforms to wavelets. Furthermore, our approach is applicable to different types of image statistics and can also serve as an optimization tool for the design of transforms/quantizers. Through the interaction of the source and quantizer models, our work provides useful insights and is instrumental in identifying and removing quantization artifacts from general signals coded with general transforms. As we decouple the modeling and processing steps, we allow for the construction of many different types of estimators depending on the desired sophistication and available computational complexity. In the low end of this spectrum, our lookup table based estimator, which can be deployed in low complexity environments, provides competitive PSNR values with some of the best results in the literature. [ABSTRACT FROM AUTHOR]

Real-time delivery of video over best-effort error- prone packet networks requires scalable erasure-resilient com- pression systems in order to 1) meet the users' requirements in terms of quality, resolution, and frame-rate; 2) dynamically adapt the rate to the available channel capacity; and 3) provide robustness to data losses, as retransmission is often impractical. Furthermore, the employed erasure-resilience mechanisms should be scalable in order to adapt the degree of resiliency against transmission errors to the varying channel conditions. Driven by these constraints, we propose in this paper a novel design for scalable erasure-resilient video coding that couples the compression efficiency of the open-loop architecture with the robustness provided by multiple description coding. In our approach, scalability and packet-erasure resilience are jointly provided via embedded multiple description scalar quantization. Furthermore, a novel channel-aware rate-allocation technique is proposed that allows for shaping on-the-fly the output bit rate and the degree of resiliency without resorting to channel coding. As a result, robustness to data losses is traded for better visual quality when transmission occurs over reliable channels, while erasure resilience is introduced when noisy links are involved. Numerical results clearly demonstrate the advantages of the proposed approach over equivalent codec instantiations employing 1) no erasure-resilience mechanisms, 2) erasure-resilience with nonscalable redundancy, or 3) data-partitioning principles. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel data-embedding system with high embedding capacity. The embedding algorithm is based on the quantized projection embedding method with some enhancement to achieve high embedding rates. In particular, our system uses a random permutation of the columns of a Hadamard matrix as projection vectors and a fixed perceptual mask based on the JPEG default quantization table for the quantization step design. As a result, the data-embedding system achieves 1/167 (1 bit out of 167 raw image bits) to 1/84 hiding ratios with a BER of around 0.1% in the presence of JPEG compression attacks, while maintaining visual distortion at a minimum. [ABSTRACT FROM AUTHOR]

A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for the MDP for a training vector only in this subset of partitions plus the single previous MDP. As the size of this subset is much smaller than the total number of codevectors, the search process is speeded up significantly. The creation of the subset is essential, as it has a direct effect on the improvement in computation time of the proposed method. The schemes that create the subset efficiently have been proposed. The proposed method generates a codebook identical to that generated using the GLA. It is simple and requires only minor modification of the GLA and a modest amount of additional memory. The experimental results show that the computation time of codebook training was improved by factors from 6.6 to 50.7 and from 5.8 to 70.4 for two test data sets when codebooks of sizes from N = 16 to 2048 were trained. The proposed method was also combined with an earlier published method to further improve the computation time. [ABSTRACT FROM AUTHOR]

Generally speaking, rate scalable video systems today are evaluated operationally, meaning that the algorithm is implemented and the rate-distortion performance is evaluated for an example set of inputs. However, in these cases it is difficult to separate the artifacts caused by the compression algorithm and data set with general trends associated with scalability. In this paper, we derive and evaluate theoretical rate-distortion performance bounds for both layered and continuously rate scalable video compression algorithms which use a single motion-compensated prediction (MCP) loop. These bounds are derived using rate-distortion theory based on an optimum mean-square error (MSE) quantizer, and are thus applicable to all methods of intraframe encoding which use MSE as a distortion measure. By specifying translatory motion and using an approximation of the predicted error frame power spectral density, it is possible to derive parametric versions of the rate-distortion functions which are based solely on the input power spectral density and the accuracy of the motion-compensated prediction. The theory is applicable to systems which allow prediction drift, such as the data-partitioning and SNR-scalability schemes in MPEG-2, as well as those with zero prediction drift such as fine granularity scalability MPEG-4. For systems which allow prediction drift we show that optimum motion compensation is a sufficient condition for stability of the decoding system. [ABSTRACT FROM AUTHOR]

The classic signal quantization problem was introduced by Lloyd. We formulate another, similar problem: The optimal mapping of digital fine grayscale images (such as 9-13 bits-per-pixel medical images) to a coarser scale (e.g., 8 bits per pixel on conventional computer monitors). While the former problem is defined basically in the real signal domain with smoothly distributed noise, the latter refers to an essentially digital domain. As we show in this paper, it is this difference that makes the classic quantization methods virtually inapplicable in typical cases of requantization of the already digitized images. We found experimentally that an algorithm based on dynamic programming provides significantly better results than Lloyd's method. [ABSTRACT FROM AUTHOR]

Due to its high image quality and moderate computational complexity, error diffusion is a popular halftoning algorithm for use with inkjet printers. However, error diffusion is an inherently serial algorithm that requires buffering a full row of accumulated diffused error (ADE) samples. For the best performance when the algorithm is implemented in hardware, the ADE data should be stored on the chip on which the error diffusion algorithm is implemented. However, this may result in an unacceptable hardware cost. In this paper, we examine the use of quantization of the ADE to reduce the amount of data that must be stored. We consider both uniform and nonuniform quantizers. For the nonuniform quantizers, we build on the concept of tone-dependency in error diffusion, by proposing several novel feature-dependent quantizers that yield improved image quality at a given bit rate, compared to memoryless quantizers. The optimal design of these quantizers is coupled with the design of the tone-dependent parameters associated with error diffusion. This is done via a combination of the classical Lloyd-Max algorithm and the training framework for tone-dependent error diffusion. Our results show that 4-bit uniform quantization of the ADE yields the same halftone quality as error diffusion without quantization of the ADE. At rates that vary from 2 to 3 bits per pixel, depending on the selectivity of the feature on which the quantizer depends, the feature-dependent quantizers achieve essentially the same quality as 4-bit uniform quantization. [ABSTRACT FROM AUTHOR]

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper. [ABSTRACT FROM AUTHOR]