IEEE Transactions on Multimedia

We propose a novel technique for steganography on 3-D meshes so as to resist steganalysis. The majority of existing methods modulate vertex coordinates to embed messages in a nonadaptive way. We take account of complexity of local regions as joint distortion of a triple unit (vertice) and coding method such as syndrome trellis codes to adaptively embed messages, which owns stronger security with respect to existing steganalysis.

In general, low-rank representation (LRR) aims to find the lowest rank representation with respect to a dictionary. In fact, the dictionary is a key aspect of low-rank representation. However, a lot of low-rank representation methods usually use the data itself as a dictionary (i.e., a fixed dictionary), which may degrade their performances due to the lack of clustering ability of a fixed dictionary.

The partition algorithm as a digital image processing technique is significant to many applications, such as data encryption, image denoising, and 3-D reconstruction. In order to achieve well partition that can availably reduce the distortion phenomenon, a novel approach named image adaptive triangular partition (IATP) is proposed, which considers the grayscale distribution of the image and removes...

The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise.

Correlation filters (CFs) have been extensively used in tracking tasks due to their high efficiency although most of them regard the tracked target as a whole and are minimally effective in handling partial occlusion. In this study, we incorporate a part-based strategy into the framework of CFs and propose a novel multipart correlation tracker with triangle-structure constraints. Specifically, we train multiple CFs for the global object and local parts, which are then jointly applied to obtain the correlation response of any candidate during tracking.

Self-learning super-resolution (SLSR) algorithms have the advantage of being independent of an external training database. This paper proposes an SLSR algorithm that uses convolutional principal component analysis (CPCA) and random matching. The technologies of CPCA and random matching greatly improve the efficiency of self-learning. There are two main steps in this algorithm: forming the training and testing the data sets and patch matching. In the data set forming step, we propose the CPCA to extract the low-dimensional features of the data set.

This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional image dehazing methods may amplify the noise depending on the distance and density of the haze. To suppress the noise and improve the dehazing performance, an imaging model is modified by adding the process of amplifying the noise in hazy conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for dehazing the image.

The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise. Afterward, we propose a statistical model describing the residual noise from a resampled image.

The scope of the Periodical is the various aspects of research in multimedia technology and applications of multimedia, including, but not limited to, circuits, networking, signal processing, systems, software, and systems integration, as represented by the Fields of Interest of the sponsors.