Ethical and legal requirements have made accessibility a crucial feature in any information systems. This paper presents a content adaptation framework, based on the MPEG-21 standard, to help low-vision users have better accessibility to visual contents. We first present an overview of MPEG-21 Digital Item Adaptation (DIA) and the low-vision description tool which enables interoperable content adaptation. This description tool lists seven low-vision symptoms, namely loss of fine detail, lack of contrast, central vision loss, peripheral vision loss, hemianopia, light sensitivity, and need of light. Then we propose a systematic contrast-enhancement method to improve the content visibility for low-vision users, focusing on the first two symptoms. The effectiveness of the low-vision description tool and our adaptation framework is verified by some experiments with an adaptation test-bed. The major advantages of the proposed approach include 1) support of a wide range of low-vision conditions, and 2) customized content adaptation to specific characteristics of each user.

The active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).

In this letter, we propose an enhanced method for inter-layer motion prediction in scalable video coding (SVC). For inter-layer motion prediction, the use of refined motion data in the Fine Granular Scalability (FGS) layer is proposed instead of the conventional use of motion data in the base quality layer to reduce the inter-layer redundancy efficiently. Experimental results show that the proposed method enhances coding efficiency without increasing the computational complexity of the decoder.

A good watermark is known to be perceptually invisible, undetectable without key and robust to spatial/temporal data modification. In this paper, we utilize the characteristics of the human visual system (HVS) for watermarking. In HVS, the response of visual cortex decomposes the image spectra into perceptual channels that are octave bands in spatial frequency. Based on the octave-bands division, same numbers of bits of the watermark are inserted into each channel. Experimental results show that the proposed method based on HVS method gives strong robustness to the attacks compared with conventional DCT, wavelet and DFT watermarking methods.

The noise robustness of an audio fingerprinting system is one of the most important issues in music information retrieval by the content-based audio identification technique. In a real environment, sound recordings are commonly distorted by channel and background noise. Recently, Philips published a robust and efficient audio fingerprinting system for audio identification. To extract a robust and efficient audio fingerprint, Philips applied the first derivative (differential) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the noise robustness of Philips' audio fingerprinting scheme is still insufficient. In this paper, we introduce an extension method of the audio fingerprinting scheme for the enhancement of noise robustness. As an alternative to frequency filtering, a type of band-pass filter, instead of a high-pass filter, is used to achieve robustness to background noise in a real situation. Our experimental results show that the proposed filter improves the noise robustness in audio identification.

Image processing in transform domain has many advantages but it could be suffered from local effects such as a blocking artifact. In this paper, an image processing is performed by weighting coefficients in the compressed domain, i.e., filtering coefficients are appropriately selected according to the processing. Since we find the appropriate factors according to global image enhancement, blocking artifacts are reduced between inter-blocks. Experimental results show that the proposed technique has the advantages of simple computation and easy implementation.

In this paper, we propose an Adaptation Decision-Taking Engine (ADTE) that targets the delivery of scalable video content in mobile usage environments. Our ADTE design relies on an objective perceptual quality metric in order to achieve video adaptation according to human visual perception, thus allowing to maximize the Quality of Service (QoS). To describe the characteristics of a particular usage environment, as well as the properties of the scalable video content, MPEG-21 Digital Item Adaptation (DIA) is used. Our experimental results show that the proposed ADTE design provides video content with a higher subjective quality than an ADTE using the conventional maximum-bit-allocation method.

Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection algorithm using adaptive contrast enhancement in a mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection algorithm includes two parts. One is adaptive contrast enhancement in which the enhancement filtering parameters are determined based on noise characteristics of the mammogram. The other is a multi-stage microcalcification detection. The results show that the proposed microcalcification detection algorithm is much more robust against fluctuating noisy environments.

The use of the homomorphic filter technique is described in order to enhance the contrast in the mammographic images, which is adopted to the dyadic wavelet transform. The proposed method has employed the nonlinear enhancement in homomorphic filtering as well as denoising method in the wavelet domains. Experimental results show that the homomorphic filtering method improves the contrast in breast tumor images such that the contrast improvement index is increased by two fold compared to the conventional wavelet-based enhancement technique.

In heterogeneous network environments, it is mandatory to measure the grade of the video quality in order to guarantee the optimal quality of the video streaming service. Quality of Service (QoS) has become a key issue for service acceptability and user satisfaction. Although there have been many recent works regarding video quality, most of them have been limited to measuring quality within temporal and Signal-to-Noise Ratio (SNR) scalability. H.264/AVC Scalable Video Coding (SVC) has emerged and has been developed to support full scalability. This includes spatial, temporal, and SNR scalability, each of which shows different visual effects. The aim of this paper is to define and develop a novel video quality metric allowing full scalability. It focuses on the effect of frame rate, SNR, the change of spatial resolution, and motion characteristics using subjective quality assessment. Experimental results show the proposed quality metric has a high correlation to subjective quality and that it is useful in determining the video quality of SVC.