We investigate the frequency sensitivity of the human visual system, which reacts differently at different frequencies in video coding. Based on this observation, we used different quantization steps for different frequency components in order to explore the possibility of improving coding efficiency while maintaining perceptual video quality. In other words, small quantization steps were used for sensitive frequency components while large quantization steps were used for less sensitive frequency components. We performed subjective testing to examine the perceptual video quality of video sequences encoded by the proposed method. The experimental results showed that a reduction in bitrate is possible without causing a decrease in perceptual video quality.
Deinterlacing has been used to convert interlaced video signals into progressive video signals. Conventional deinterlacing methods with low levels of complexity can lead to deteriorating video quality, particularly in edge areas. Another problematic artifact is flickering, which occurs more frequently as the video size increases. For example, this flickering artifact is more frequently observed in high-definition television signals. In this paper, we propose a flickering artifact reduction algorithm for deinterlacing, which noticeably increases the overall visual quality.
As more multimedia services have become increasingly available over networks where bandwidth is not always guaranteed, quality monitoring has become an important issue. For instance, quality of experience and quality monitoring have become important problems in internet protocol television applications, since transmission errors may introduce all kinds of additional video quality degradations. In this paper, we present a reduced-reference objective model for video quality measurements in multimedia applications. The proposed method first measures edge degradations that are critical for perceptual video quality and then considers transmission error effects. We compared the proposed method with some existing methods. Independent verifications confirmed that the proposed method showed good performance and consequently it was included in an International Telecommunication Union recommendation. The proposed method can be used to monitor video quality at receivers while requiring minimum usage of additional bandwidth.
In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.
In this paper, we present a new panchromatic sharpening method based on quality parameter optimization. Traditionally,
quality metrics such as UIQI, CORR, and ERGAS have been used to assess the quality of panchromatic sharpening.
Generally, HPF (high pass filtering) based panchromatic sharpening methods produce good performance. However, one
problem with these methods is the peak noise that arises due to a small denominator value when the mean shift problem
is addressed. In order to address this problem, we introduce an offset value that was optimized based on a quality metric.
We assumed that the offset value was invariant with respect to the spatial scale, and it was used to enhance the resolution
of the original multispectral images by using a high-resolution panchromatic image. The experimental results
demonstrate that the proposed method showed better performance than some existing panchromatic sharpening methods.
Most current TV broadcasting systems use the interlaced scanning scheme in order to reduce bandwidth. However, this
interlaced scan may introduce undesirable artifacts. A large number of deinterlacing methods have been proposed.
Traditionally, the performance of deinterlacing methods has been measured in terms of pixel differences. However, there
are some impairments which may not be accurately reflected in PSNR or MSE. In this paper, we choose several
deinterlacing methods and apply them to a set of video sequences. Then, we perform subjective tests for the deinterlaced
video sequences. The perceptual video quality scores are presented along with some discussions.
Deinterlacing is a process of converting interlaced video signals into progressive video signals. Traditionally,
deinterlacing methods have been evaluated in terms of PSNR. On the other hand, some artifacts can severely degrade
perceptual video quality, but may not produce large mean square errors. One of such artifacts is flickering. This
flickering artifact occurs more frequently as the video size increases. For example, for HD TV signals, this flickering
artifact is more frequently observed. In this paper, we propose a deinterlacing method which noticeably reduces this
flickering artifact with slight loss in terms of PSNR.
In this paper, we investigate automated display methods for hyperspectral images with unsupervised segmentation.
First, we apply an unsupervised segmentation method, which will produce a number of unlabeled classes. Then, we
choose the classes whose sizes are larger than a threshold value. Then, we apply a feature extraction method to the
chosen classes and find dominant discriminant features, which are used to display the hyperspectral images. We also
exploit the use of the principal component analysis for the display of hyperspectral images. Experimental images show
that the color images produced by the proposed methods show interesting characteristics compared to the conventional
In this paper, we propose a bit allocation method for 2D compression of hyperspectral images to enhance classification
performance. First, we select a number of classes from original hyperspectral images. It is noted that the classes can be
automatically selected by applying an unsupervised segmentation method. Then, we apply a feature extraction method
and determine discriminately dominant feature vectors. By examining the feature vectors, we determine the discriminant
usefulness of each spectral band. Finally, based on the discriminant usefulness of the spectral bands, we determine bit
allocation of each spectral band. Experimental results show that it is possible to enhance the discriminant information at
the expense of PSNR. Depending on applications, one can either minimize the mean squared error or choose to preserve
the classification capability of the hyperspectral images.
For most video quality measurement algorithms, a processed video sequence and the corresponding source video
sequence need to be aligned in the spatial and temporal directions. Furthermore, when the source video sequences are
encoded and transmitted, gain and offset can be introduced. The estimation process, which estimates spatial shifts,
temporal shift, gain and offset, is known as video calibration. In this paper, we proposed a video calibration method for
full-reference and reduced-reference video quality measurement algorithms. The proposed method extracts a number of
features from source video sequences. Using these features, we perform video calibration. Experimental results show that
the proposed method provides good performance and the proposed method was included in an international standard.
In this paper, we propose an efficient compression method for sampled color images obtained using a single sensor with a color filter array. The proposed method directly compresses the output of the single image sensor without first interpolating missing colors of sampled color images. Since the amount of the original image data of the single image sensor is smaller than that of the full color image data, the size of compressed data can be smaller and encoding can be more efficient. Experimental results show that the proposed method provides improved performance compared to existing method.