Many of portable devices such as smart phones, portable multimedia players (PMP), and digital single-lens reflex (DSLR) cameras are capable of capturing high-resolution images (e.g. 10 mega-pixel in DSLR) or even video. The limited battery power supply in the portable devices often prevents these systems to use high-power large liquid crystal display (LCD). Instead, the portable devices often have a LCD screen with small physical size (e.g. 3 cm × 2 cm for Smartphone or DSLR) and with much lower pixel resolution (e.g. 0.15 mega-pixel for 480 × 320 display) than actual image/video resolution. Thus, the high-resolution image and video are down-sampled before being displayed. Unfortunately, the anti-aliasing filter often leads to rather severe blurring. Although the blurring may be minor when the viewing distance is large, it can be rather disturbing in portable applications due to the short viewing distance. To cope with the blurring problem, one possible solution is to use an LCD screen with higher resolution. But such hardware solution tends to be expensive and often not welcomed by the consumer electronic companies. Another possible solution is to continue to use the low-resolution LCD screen, but use some software technique to enhance the apparent image/video resolution. In this paper, we discuss a novel way to improve the apparent resolution of down-sampled image/video using a technique called subpixel rendering, which controls subpixel that is smaller than a pixel in a high-precision manner.

The broad availability of tools for the acquisition and processing of multimedia signals has recently led to the concern that images and videos cannot be considered a trustworthy evidence, since they can be altered rather easily. This possibility raises the need to verify whether a multimedia content, which can be downloaded from the internet, acquired by a video surveillance system, or received by a digital TV broadcaster, is original or not. To cope with these issues, signal processing experts have been investigating effective video forensic strategies aimed at reconstructing the processing history of the video data under investigation and validating their origins. The key assumption of these techniques is that most alterations are not reversible and leave in the reconstructed signal some “footprints”, which can be analyzed in order to identify the previous processing steps. This paper presents an overview of the video forensic techniques that have been proposed in the literature, focusing on the acquisition, compression, and editing operations, trying to highlight strengths and weaknesses of each solution. It also provides a review of simple processing chains that combine different operations. Anti-forensic techniques are also considered to outline the current limitations and highlight the open research issues.

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

FTV (free-viewpoint television) is an innovative visual media that allows users to view a three-dimensional (3D) scene by freely changing their viewpoints. Thus, it enables realistic viewing and free navigation of 3D scenes. FTV is the ultimate 3DTV with infinite number of views and ranked at the top of visual media. FTV is not a conventional pixel-based system but a ray-based system. New types of ray capture, processing, and display technologies have been developed for FTV. These technologies were also used to realize an all-around ray-reproducing 3DTV. The international standardization of FTV has been promoted in MPEG. The first phase of FTV ismulti-view video coding and the second phase is 3D video. In this paper, the FTV system and its technologies are reviewed.

This paper focuses on applications of Bayesian approaches to acoustic modeling for speech recognition and related speech-processing applications. Bayesian approaches have been widely studied in the fields of statistics and machine learning, and one of their advantages is that their generalization capability is better than that of conventional approaches (e.g., maximum likelihood). On the other hand, since inference in Bayesian approaches involves integrals and expectations that are mathematically intractable in most cases and require heavy numerical computations, it is generally difficult to apply them to practical speech recognition problems. However, there have been many such attempts, and this paper aims to summarize these attempts to encourage further progress on Bayesian approaches in the speech-processing field. This paper describes various applications of Bayesian approaches to speech processing in terms of the four typical ways of approximating Bayesian inferences, i.e., maximum a posteriori approximation, model complexity control using a Bayesian information criterion based on asymptotic approximation, variational approximation, and Markov chain Monte Carlo-based sampling techniques.

We propose the notion of latent acoustic topics to capture contextual information embedded within a collection of audio signals. The central idea is to learn a probability distribution over a set of latent topics of a given audio clip in an unsupervised manner, assuming that there exist latent acoustic topics and each audio clip can be described in terms of those latent acoustic topics. In this regard, we use the latent Dirichlet allocation (LDA) to implement the acoustic topic models over elemental acoustic units, referred as acoustic words, and perform text-like audio signal processing. Experiments on audio tag classification with the BBC sound effects library demonstrate the usefulness of the proposed latent audio context modeling schemes. In particular, the proposed method is shown to be superior to other latent structure analysis methods, such as latent semantic analysis and probabilistic latent semantic analysis. We also demonstrate that topic models can be used as complementary features to content-based features and offer about 9% relative improvement in audio classification when combined with the traditional Gaussian mixture model (GMM)–Support Vector Machine (SVM) technique.