With the development of a mobility society, supporting drivers from the mental aspect for safe driving is an increasingly important issue before self-driving cars are dominant. We have developed a sensor network that uses biosignal sensors such as EEG, ECG, heart rate, and drivers’ operation of pedals and steering to measure drivers’ emotions. However, even though EEG may be an excellent index to estimate emotion, it is not practical to wear an EEG sensor while driving. So, we would like to add other methods that are easy to use and can support estimating drivers’ emotions. In this paper, we report the result of using the facial expression analysis technique to estimate a car driver’s stress and fatigue. The result shows that facial expression reflects driver emotion in most cases and is closely related to automotive operating status.

Multispectral imaging with a multispectral filter array (MSFA) facilitates snapshot imaging; however, a demosaicking process is required to estimate a fully defined multispectral image based on undersampled sensor data. Undersampling induces aliasing and adverse artifacts in the reconstructed image. To solve this problem, Jia et al. proposed the Fourier spectral filter array (FSFA), which can reduce aliasing. In this paper, we analyze the FSFA and a more generalized anti-aliasing MSFA, and we identify the property that makes MSFAs anti-aliasing. Furthermore, we propose a novel demosaicking method that is a hybrid of frequency-decomposition-based and compressive-sensing-based demosaicking. Anti-aliasing MSFAs enable demosaicking to comprehend the precise spatial structures of an image. The image assists our proposed method in precisely reconstructing images using compressive sensing. Our experimental results demonstrated that the proposed method performs better than the existing demosaicking methods, especially in terms of spatial reconstruction.

In this paper, we propose a reversible data hiding (RDH) method based on a two-dimensional (2D) histogram and generalized histogram shifting (GHS). First, a cover image is decomposed into wavelet subbands using the integer-to-integer wavelet transform (I2I-WT). Then, the 2D histogram is generated by counting the occurrence frequency of the pair of wavelet coefficients. Here, the pair of wavelet coefficients denotes two wavelet coefficients located in the same position in the selected two subbands. In the last step, RDH based on GHS, which embeds the multilevel symbols in the cover image is applied. The experimental results show that the proposed method achieves a higher payload capacity than the conventional RDH methods.

Conventional RGB image acquisition employs an image capturing system that utilizes color filter array (CFA) technology; however, it is limited in its ability to represent visible colors. Multispectral imaging based on filter array architecture is required for general image capturing because of its exquisite color representation system. However, it has several issues associated with spatial and spectral resolution. In this paper, we propose a new demosaicking method that improves reconstructed image quality by considering inter-channel correlation. Our proposed method strengthens the cross-correlation of demosaicked channels by repeating interpolations. Experimental results show that our proposed method generates better quality reconstructed images than conventional methods.

In this paper, we propose a reversible data hiding (RDH) method based on a two-dimensional wavelet coefficient histogram (2D WCH) in the wavelet domain. First, a cover image is decomposed into wavelet subbands using the invertible integer-to-integer wavelet transform (121-WT). Then, the 2D WCH is generated by counting the occurrence frequency of the wavelet coefficient pairs which denote two wavelet coefficients located in the same position in the selected two subbands where the secret message is embedded. By using the 2D WCH, the correlation between the selected subbands is more effectively utilized than the traditional ID histogram. The proposed method embed the secret message reversibly in the cover image by expanding the 2D WCH. In order to embed the secret message as efficient as possible, the expansion rule for 2D WCH is proposed. Moreover, the coefficient pair selection (CPS), which the coefficients embedding the data are selected in order to modify only the selected coefficients, is implemented before generating the 2D WCH. In the experiment, the proposed method is compared with the conventional RDH methods in terms of the capacity-distortion curve.

Several color-to-gray image mapping (CGIM) methods were proposed so far to restore a color image from its corresponding printed grayscale image. They are based on embedding chrominance values into high-frequency subbands of the luminance signal. In the conventional methods, textures often become noticeable in the chrominance-embedded gray image since subsampled chrominance values are embedded directly. As a result, qualities of the chrominance-embedded grayscale images are degraded especially for images with rich textures. In this paper, we propose an improved CGIM method using DC level shifting and directional transform. In the experimental results, our method greatly improves the qualities of both the chrominance-embedded grayscale and the restored color images in comparison with conventional methods.

In distributed video coding (DVC), a low resolution video sequence is usually generated with spatial and/or temporal downsampling at an encoder. At a decoder side, interpolation is performed and the interpolated pixels are further refined by using an error-correcting code such as Turbo codes or LDPC. In our previous work, we proposed a spatial domain DVC which uses a line-based downsampling method called selective data pruning (SDP). SDP is used for reducing the spatial frame size before compression. After decoding, received frames are decoded and interpolated back to their original size by high-order edge-directed interpolation (HEDI). In this paper, we propose a modified high-order edge-directed interpolation (M-HEDI) to avoid interpolation artifacts. Experimental results show that the interpolated frames by using M-HEDI have higher quality than those using HEDI. The proposed method outperforms conventional DVCs and the video coding standard H.264/AVC in majority of all the test video sequences.

Mosaicked color filter arrays and demosaicking methods for multispectral images have been proposed in recent years. Several studies have evaluated the multispectral filter array (MSFA) pattern, but a method to optimize the pattern has not been proposed. We focus on the spatial arrangement of MSFAs to improve the demosaicked image quality. To evaluate the filter arrangement, we propose a new metric that uses both the spatial and spectral correlation of the filters. The arrangement is optimized by using simulated annealing and the proposed metric. An advantage of the proposed metric and its optimization is that they do not need to use image data. Experimental results show that the new metric is proportional to the peak-to-signal noise ratio (PSNR) and that a higher PSNR can be obtained by minimizing the metric using simulated annealing.

Several color-to-gray mapping methods were proposed to restore a color image from its corresponding grayscale image. These methods are based on embedding chrominance signals into high-frequency subbands of the luminance signal. In the conventional methods, only separable filters are used for the subband decomposition. In addition, subbands at fixed positions are always used to embed chrominance signals. However, in many images, diagonal components are included as well as horizontal/vertical ones. Therefore, qualities of chrominance-embedded grayscale and restored color images are sometimes degraded especially for images with rich textures. In this paper, we propose an improved color restoration method using a directional transform named hybrid wavelets and directional filter banks which considers directionality in images. Experimental results show our method improves the image qualities of the restored color images as well as the chrominance-embedded grayscale images.

We propose a low-complexity, pixel-based watermarking method utilizing the edge areas in an image. In this method, an image is divided into blocks and the blocks are classified as edge blocks and non-edge blocks. The histogram of pixels in an edge block is a bi-modal distribution which has two peaks. Therefore, these pixels are further classified into two classes named “high peak class” and “low peak class”. Then, a bit of the watermark is embedded by controlling the between-class variance by changing the pixel values in each class of the edge block. We utilized the discriminant analysis method for deciding a threshold and controlling the between-class variance. The proposed method can improve the image quality while maintaining robustness against compression and smoothing. In this paper, we evaluated the image quality of watermarked images using PSNR and SSIM. We also evaluated robustness against JPEG compression and Gaussian filtering.

This paper proposes an image coding method incorporated with content-aware image resizing. It is a promising application of our previously proposed rate-dependent seam carving (RD-SC). RD-SC is combined with lifting-based discrete wavelet transform to construct a new multiresolution image representation. Furthermore, the modified parent-children relationship of SPIHT is shown. Our proposed image coding presents comparable performances to the conventional SPIHT even for the original size, and retargeted image quality is satisfactory.

In this paper, we present a seam carving, which is a well-known content-aware image resizing method, with a constraint on bitrates for seam path information (SPI). The SPI corresponds to pixel positions to be pruned and it generally requires high bitrate when we store or transmit it. However, the SPI should be transmitted to re ceivers in the case that the receivers are devices with low computing power, such as cell phones and PDAs since seam carving at the receivers is a computationally-demanding process. We resolve the problem by applying piecewise linear approximation to the seam paths. In the experimental results, the retargeted images yielded by the original seam carving and the proposed one are very similar, whereas required bitrate for the SPI in the proposed method is 34-97 % less than the original one.

In this paper, we extend a rate-dependent content-aware image retargeting method, generalized selective data pruning, to video sequence. It is regarded as a spatial-domain video retargeting, i.e., reduction of the spatial video size, with a rate-dependent property for side information. It is realized by the piecewise linear approximation of seam carving. The side information bitrate for video retargeting can vary depending on the control parameter and is negligible to transmit with the encoded video bitstream while the method maintains video retargeting performance.

In current distributed video coding (DVC), interpolation is performed at the decoder and the interpolated pixels are reconstructed by using error-correcting codes, such as Turbo codes and LDPC. There are two possibilities for downsampling video sequences at the encoder: temporally or spatially. Traditionally temporal downsampling, i.e., frame dropping, is used for DVC. Furthermore, those with spatial downsampling (scaling) have been investigated. Unfortunately, most of them are based on uniform downsampling. Due to this, details in video sequences are often discarded. For example, edges and textured regions are difficult to interpolate, and thus require many parity bits to restore the interpolated portions for the spatial domain DVC. In this paper, we propose a new spatial domain DVC based on adaptive line dropping so-called selective data pruning (SDP). SDP is a simple nonuniform downsampling method. The pruned lines are determined to avoid cutting across edges and textures. Experimental results show the proposed method outperforms a conventional DVC for sequences with a large amount of motions.

Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. In this paper, we incorporate image concentration/dilution process in image coding. An image retargeting method called seam carving is used for image concentration. Furthermore, it is modified to yield rate-distortion optimized seams by a hierarchical search process. Our method can be incorporated into any image encoder since the image concentration (dilution) is a pre(post)- processing of image encoder (decoder). In the experimental results, JPEG/SPIHT with image concentration/dilution presents significant bitrate savings compared with the original JPEG/SPIHT alone and reconstructed image qualities are very similar to each other.

In this paper, an improved method of image concentration for image coding with concentration and dilution is presented. Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. The authors have proposed an image coding approach based on the image concentration and dilution. This paper focuses on an improvement of image concentration to enhance quality of the reconstructed (diluted) image. In the experimental results, the improved method obtains bitrate savings compared with image encoders alone, and the reconstructed images do not have perceptually noticeable artifacts.

Adaptive directional wavelet transform is an effective alternative of the traditional 2-D wavelet transform for image coding. It is able to transform an image adaptively along diagonal orientations as well as conventional vertical/horizontal directions. However, it requires to transmit transform direction information to the decoder side. For image coding at very low bitrates, the bit budget of the direction information degrades a reconstructed image quality. In this paper, a method to construct a scalable bitstream for transform directions is presented. We utilize the fact that the matrix yielded by transform direction indices still contains the original image characteristics. The matrix is transformed by a block-lifting based DCT, then encoded by SPIHT to yield a scalable bitstream. Our method is effective for very low bitrate image coding, and is comparable to the non-scalable one for middle-to-high bitrates.

In this paper, we propose a data embedding method into still images based on Vector Quantization (VQ). In recent years, several VQ-based data embedding methods have been proposed. For example, `Mean Gray-Level Embedding method (MGLE)' are `Pair wise Nearest-Neighbor Embedding method (PNNE)' are simple, but not sufficiently effective. Meanwhile, an efficient adaptive data hiding method called `Adaptive Clustering Embedding method (ACE)' was proposed, but is somewhat complicated because the VQ indices have to be adaptively clustered in the embedding process. In our proposed method, output vectors are considered as nodes, and nodes are linked as a tree structure and information is embedded by using some of linked vectors. The simulation results show that our proposed method indicates higher SNR than the conventional methods under the same amounts of embedded data.

Graphical passwords are an authentication method that uses pictures as passwords instead of using alphanumeric characters. We propose a graphical password method which is difficult to steal original pass-image by using characteristics of human vision system. In our method, we combine low frequency components of a decoy picture with high frequency components of a pass-image. It is easy for legitimate users to recognize the pass-image in the blended image. On the other hand, this task is difficult for attackers. We used discrete wavelet transform (DWT) to blend a decoy image and a pass-image. User studies are conducted to evaluate memorability and shoulder-surfing robustness of this method. We also compared our method with other existing methods in terms of the authentication time and the success ratio by the user test. The results show that our method is more usable and secure against shoulder-surfing.

This paper proposes an efficient approach for adaptive directional wavelet transform (WT) based on directional prefiltering. Although the adaptive directional WT is able to transform an image along diagonal orientations as well as traditional horizontal and vertical directions, it sacrifices computation speed for good image coding performance. We present two efficient methods to find the best transform directions by prefiltering using 2-D filter bank or 1-D directional WT along two fixed directions. The proposed direction calculation methods achieve comparable image coding performance comparing to the conventional one with less complexity. Furthermore, transform direction data of the proposed method can be used for content-based image retrieval to increase retrieval ratio.

This paper proposes a computationally efficient approach of adaptive directional wavelet transform (AD WT). The AD WT is based on lifting implementation of WT, and it is able to transform an image along diagonal orientations as well as traditional horizontal and vertical directions. The AD WT sacrifices computational speed for its good image coding performance. We present an alternative method to find the best transform directions by pre-directional filterings for the AD WT. The proposed direction calculation method shows very comparable image coding performance to the conventional one, whereas its computational cost is relatively very low.

In this paper, a calculation method of transform directions for adaptive directional wavelet transform (AD WT) is proposed. It uses 2-D highpass filters as a preprocessing of the conventional calculation stage. The filters can reduce the number of direction candidates effectively. In image coding, our proposed framework shows very comparable results to the conventional AD WT and outperforms the traditional separable WT.

No abstract available.

Most of watermarking methods are either robust watermarking for copyright protection or fragile watermarking for tamper detection. We propose a watermarking method that has both robustness against image processing and ability of tamper detection. In the proposed method, three wavelet coefficients are treated as a vector and one bit of a watermark is embedded by using order relationships among the coefficients. The number of order relationships among three coefficients is six, while only two patterns of relationships are needed to represent one bit of a watermark. Order relationships that are not assigned to a watermark bit can be used for tamper detection.

Studies of digital museum are making progress in the various fields. Especially, in case of preserving 3-D objects, it is necessary to take digital photographs from various viewpoints. Thus, the amount of data becomes huge and a data compression technique should be introduced for archiving them. Moreover, in order to represent art works faithfully, a lossless coding method is indispensable. This study aims to construct an encoding and a browsing scheme that enables free handling according to user's demands for such images. In this paper, we propose a new lossless coding scheme for digital image sequences which were acquired from different perspectives. In such case, the neighboring frames have high correlation. Our proposed coding method is based on a predictive model with the shape information and the rotation compensation. Simulation results show that our proposed scheme is effective for coding of digital image sequences of convex objects.

Multimedia communication systems using super high definition (SHD) images are widely desired in various communities such as medical imagery, digital museum and libraries and so on. There are, however, many problems in SHD image communication systems, because of high pixel accuracy and high resolution. We considered indispensable functions to be realized in SHD image application systems. These are summarized to three items, i.e. reversibility, scalability and progressibility. This paper proposes a partially decodable coding method for realizing the scalability function. That is to say, when a whole image cannot be displayed on the monitor, a reduced image is displayed at first to select a region of interest. Then image data of the selected region is extracted from the code stream. For this purpose, a kind of partially decodable code should be introduced. We propose a new partially decodable coding method based on Golomb-Rice code.