Content-aware image resizing (CAIR) is desired because it preserves prominent regions in a resized image. However, CAIR requires high computational complexity to perform in mobile devices, though it is desired for these displays. Moreover, transmitting the side information for CAIR from the encoder is a problem since it usually requires high bitrates compared with those for image data. In this paper, we present a rate-dependent CAIR method that produce various retargeting results based on the bitrates for side information. Furthermore, we apply the proposed technique to wavelet-based image coding. Our proposed content-aware image coding method provides good performances for both CAIR and image coding.

Within the past decade, analyzing and modeling human behavior by processing large amounts of collected data has become an active research field in the area of human–machine interaction. The research community is striving to find principled ways to explain and represent important behavioral characteristics of humans, with the goal of developing more efficient and more effective cooperative interactions between humans, machines, and environment. This paper provides a summary of the progress we have achieved to date in our study, which has focused specifically on interactions between driver, vehicle, and driving environment. First, we describe the method of data collection used to develop our on-the-road driving data corpus. We then provide an overview of the data-driven, signal processing approaches we used to analyze and model driver behavior for a wide range of practical vehicle applications. Next, we perform experimental validation by observing the actual driving behavior of groups of real drivers. In particular, the vehicle applications of our research include driver identification, behavior prediction related to car following and lane changing, detection of emotional frustration, and improving driving safety through driver coaching. We hope this paper will provide some insight to researchers with an interest in this field, and help identify areas and applications where further research is needed.

A frame quality control mechanism for H.264/AVC is proposed in this research. The research objective is to ensure that a suitable quantization parameter (QP) can be assigned to each frame so that the target quality of each frame will be achieved. One of the potential application is consistently maintaining  frame quality during the encoding process to facilitate video archiving and/or video surveillance. A single-parameter distortion to quantization (D–Q) model is derived by training a large number of frame blocks. The model parameter can be determined from the frame content before the exact encoding process. Given the target quality for a video frame, we can then select an appropriate QP according to the proposed D–Q model. Model refinement and QP adjustment of subsequent frames can be applied by examining the coding results of previous data. Such quality measurements as peak signal to noise ratio (PSNR) and structural similarity (SSIM) can be employed. The experimental results verify the feasibility of the proposed constant quality video coding framework.

Research on visual quality assessment has been active during the last decade. In this work, we provide an in-depth review of recent developments in the field. As compared with existing survey papers, our current work has several unique contributions. First, besides image quality databases and metrics, we put equal emphasis on video quality databases and metrics as this is a less investigated area. Second, we discuss the application of visual quality evaluation to perceptual coding as an example for applications. Third, we benchmark the performance of state-of-the-art visual quality metrics with experiments. Finally, future trends in visual quality assessment are discussed.

In this paper, we propose a reversible color transform (RCT) for color images acquired through a Bayer pattern color filter array. One existing RCT with fixed coefficients is simple to implement. However, it is not adaptive to each of input images. Another existing RCT based on eigenvector of covariance matrix of color components, which is equivalent to Karhunen–Loève transform (KLT), is adaptive. However, it requires heavy computational load. We remove a redundant part of this existing method, utilizing fixed statistical relation between two green components at different locations. Comparing to the KLT-based existing RCT, it was observed that the proposed RCT keeps adaptability and has better coding performance, even though its computational load is reduced.

A novel technique based on dynamic stochastic resonance (DSR) in discrete cosine transform (DCT) domain has been proposed in this paper for the enhancement of dark as well as low-contrast images. In conventional DSR-based techniques, the performance of a system can be improved by addition of external noise. However, in the proposed DSR-based work, the intrinsic noise of an image has been utilized to create a noise-induced transition of a dark image to a state of good contrast. The proposed technique significantly enhances the image contrast and color information without losing any image or color data by optimization of bistable system parameters. The performance of the proposed methodology has been measured in terms of relative contrast enhancement factor, perceptual quality measure, and color enhancement factor. When compared with the existing enhancement techniques, such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multicontrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition, automatic control of imaging tool, and various spatial/frequency-domain SR-based techniques, the proposed technique gives remarkable performance in terms of contrast and color enhancement while ascertaining good perceptual quality.