Steganography is the art and science of hiding data within innocent-looking objects (cover objects). Multimedia objects such as images and videos are an attractive type of cover objects due to their high embedding rates. There exist many techniques for performing steganography in both the literature and the practical world. Meanwhile, the definition of the steganographic capacity for multimedia and how to be calculated has not taken full attention. In this paper, for multivariate quantized-Gaussian-distributed multimedia, we study the maximum achievable embedding rate with respect to the statistical properties of cover objects against the maximum achievable performance by any steganalytic detector. Toward this goal, we evaluate the maximum allowed entropy of the hidden message source subject to the maximum probability of error of the steganalytic detector which is bounded by the KL-divergence between the statistical distributions for the cover and the stego objects. We give the exact scaling constant that governs the relationship between the entropies of the hidden message and the cover object.

Near-infrared (NIR) band sensors capture achromatic images that contain complementary details of a scene which are diminished in visible (VS) band images when the scene is obscured by haze, mist, or fog. To exploit these complementary details, special cameras are produced that can capture both VS and NIR bands. Additionally, several sophisticated VS-NIR fusion approaches have been proposed to produce enhanced VS image with high quality. However, these approaches usually encounter heavy computations that are performed off cameras, limiting its suitableness for real-time applications. In this paper, we present an efficient design and FPGA implementation of VS-NIR fusion based image enhancement approach. To be suitable for camera hardware integration and to meet video real-time requirements, the proposed implementation is designed such that the consumed FPGA resources and the number of clock cycles required for producing the fused image are kept small. To achieve this, data and calculations are reused whenever possible in addition to the parallel and pipelined operations at both data and task levels. The proposed implementation is synthesized and validated on a low-end FPGA device. The proposed implementation produces a real time, indistinguishable, and 5 times faster fused image compared with optimized C++ implementation executed on a modern computer.

Pan-sharpening is a common postprocessing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regularization term. The objective function differs from and improves upon previously reported model-based optimization approaches to pan-sharpening in two major aspects: 1) a new regularization term is introduced and 2) a highpass filter, complementary to the lowpass filter for the LR spectral observations, is introduced for the residual error corresponding to the panchromatic observation model. To obtain pan-sharpened images, an iterative algorithm is developed to solve the proposed joint minimization. The proposed algorithm is compared with previously proposed methods both visually and using established quantitative measures of SNR, spectral angle mapper, relative dimensionless global error in synthesis, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods. A software implementation is provided.

Pan-sharpening is a common post-processing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. Previously proposed pan-sharpening techniques operate on a per-channel basis, sharpening each multispectral band independently based on the panchromatic image, often in an ad hoc manner. In contrast with most prior techniques, we formulate pan-sharpening as the problem of jointly estimating the high resolution multispectral images to minimize the combined squared residual error in physically motivated observation models of the low resolution multispectral and the high resolution panchromatic images. To realize pan-sharpening using our proposed formulation, we develop an iterative algorithm to solve the joint minimization resulting in an overall algorithm with modest computational complexity. We evaluate our proposed algorithm and benchmark it against previously proposed methods using established quantitative measures of SNR, SAM, ERGAS, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods.

This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video which operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, we target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their associated macroblock prediction error, which is different from the approaches based on the motion vector attributes such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve robustness while maintaining a low prediction error level. The secret message bitstream is embedded in the least significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based on the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vector attribute-based method from the literature.

Regularization is one of the most promising methods for image up-sampling, which is an ill-posed inverse problem. A key element of such a regularization approach is the observation model relating the observed lower resolution (LR) image to the desired higher resolution (HR) up-sampled image, used in the data-fidelity term of the regularization cost function. This paper presents an algorithm to determine this observation model based on a model of the physical acquisition process for the LR image, and the ideal acquisition process for the desired HR image, both from the same underlying continuous image. The method is illustrated with typical scenarios corresponding to LR and HR cameras modeled by either Gaussian or rectangular apertures. Experiments with some regularized image up-samplers demonstrate the importance of using the correct, adapted observation model as determined by our algorithm. Index Terms-Camera aperture, data fidelity, image up-sampling, interpolation, multidimensional signal processing, observation model, power spectral density (PSD), super-resolution.

This paper presents a new formulation of the regularized image up-sampling problem that incorporates models of the image acquisition and display processes. We give a new analytic perspective that justifies the use of total-variation regularization from a signal processing perspective, based on an analysis that specifies the requirements of edge-directed filtering. This approach leads to a new data fidelity term that has been coupled with a total-variation regularizer to yield our objective function. This objective function is minimized using a level-sets motion that is based on the level-set method, with two types of motion that interact simultaneously. A new choice of these motions leads to a stable solution scheme that has a unique minimum. One aspect of the human visual system, perceptual uniformity, is treated in accordance with the linear nature of the data fidelity term. The method was implemented and has been verified to provide improved results, yielding crisp edges without introducing ringing or other artifacts.

We show how to design an optimal camera aperture for a given display aperture defined on an arbitrary spatial or spatio-temporal sampling lattice. This is achieved by determining the reciprocal Riesz basis for the vector space of displayed images. The result can be used to specify ideal virtual cameras for a number of applications such as regularized image up-sampling. A case study for displaying a hexagonally sampled image on a cathode ray tube is presented.

The moving average is often used as an observation model for image interpolation, but is it a correct and accurate model for most circumstances? Are there other options? We present a novel theoretical analysis of the regularized image up-sampling problem focusing on the data fidelity term. We start with formulation of the physical acquisition processes the image has undergone and develop a generalized design for the correct and accurate data fidelity term for regularized image up-sampling.

This paper presents a new formulation of the regularized image up-sampling problem that incorporates models of the image acquisition and display processes. This approach leads to a new data fidelity term that has been coupled with a bounded-total-variation regularizer to yield our objective function. This objective function is minimized using the level-set method with two types of motion that interact simultaneously. The method was implemented and has been verified to provide good results, yielding crisp edges without introducing ringing or other artifacts.