In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The Wyner-Ziv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve simi...

Nowadays, almost all images acquired are in color format. Traditional methods process color images by either transforming them into gray scale or dividing them into red, green, and blue components for independent processing, which is definitely not effective in representing color information. Recently, a novel Polar Linear Canonical Transform (PLCT) with parameters in SL(2,<) has been reported,...

In this paper, we address the problem of scrambling regions of interest in a video sequence. We target applications such as video surveillance preserving privacy, anonymous video communications, or TV news safeguarding the anonymity of a source. We propose an efficient solution based on transform-domain scrambling. More specifically, the sign of selected transform coefficients is flipped during...

Direct-sequence spread spectrum (DSSS) signaling techniques are often used in conjunction with digital signal processors to combat undesired interference in both military and civilian communications systems. Although spread spectrum signals are inherently robust with respect to non-white interference, the extent to which such interference aaects communications reliability depends on the system'...

This paper applies blind source separation or independent component analysis for images that may contain mixtures of text, audio, or other images for steganography purposes. The paper focuses on separating mixtures in the transform domain such as Fourier domain or the Wavelet domain. The study addresses the effectiveness of steganography when using linear mixtures of multimedia components and t...

The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised DA by combining domain alignment along with similarity-based embedding. We also introduce a validation procedure to estimate the model parameters for the NET algorithm us...

Transform-domain adaptive lters refer to LMS lters whose inputs are preprocessed with a unitary data-independent transformation followed by a power normalization stage. The transformation is typically chosen to be the discrete Fourier transform (DFT), although other transformations such as the cosine transform (DCT), the Hartley transform (DHT), or the Walsh-Hadamard transform, have also been p...

Many signal and image processing applications have benefited remarkably from the fact that the underlying signals reside in a low dimensional subspace. One of the main models for such a low dimensionality is the sparsity one. Within this framework there are two main options for the sparse modeling: the synthesis and the analysis ones, where the first is considered the standard paradigm for whic...

An on-line transform domain Least Mean Square (LMS) algorithm based on a neural approach is proposed. A temporal Principal Component Analysis (PCA) network is used as an orthonormalization layer in the transform domain LMS filter. Since PCA learning is an on-line learning algorithm, an on-line transform domain LMS filter can be easily implemented. Moreover, a modified Kalman estimation, which c...

Digital Image Watermarking is used for copyright protection of digital information. With the widespread of internet, the intellectual properties are accessible and manipulated easily. It demanded to have different ways to protect data. Digital watermarking provides a viable and promising solution. In this paper, we have described about the three different watermarking techniques (LSB, DCT, DWT)...