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.
Most artificial neural networks use a nonlinear activation function that includes sigmoid and hyperbolic tangent functions. Most artificial networks employ nonlinear functions such as these sigmoid and hyperbolic tangent functions, which incur high complexity costs, particularly during hardware implementation. In this paper, we propose new polynomial approximation methods for nonlinear activation functions that can substantially reduce complexity without sacrificing performance. The proposed approximation methods were applied to pattern classification problems. Experimental results show that the processing time was reduced by up to 50% without any performance degradations in terms of computer simulation.
In this paper, we investigate the frequency sensitivity of the human visual system. The human visual system reacts
differently at different frequencies. Based on this observation, we used different quantization steps for different
frequency components 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.
Recently, high quality video services have become widely available. To transmit or store these HD video programs,
compression is required and various lossy compression schemes have been developed. On the other hand, there are some
applications which require lossless compression. However, most conventional lossless coding methods have high
complexity and require a long processing time. In this paper, a parallel lossless compression algorithm with low
complexity is proposed. The proposed compression algorithm reduced HD video sequences about by half. Furthermore,
the processing time was significantly reduced when using a GPGPU. The algorithm can be implemented in real time for
HD video sequences.
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.
Neural network de-interlacing has shown promising results among various de-interlacing methods. In this paper, we
investigate the effects of input size for neural networks for various video formats when the neural networks are used for
de-interlacing. In particular, we investigate optimal input sizes for CIF, VGA and HD video formats.
Interlaced scanning has been widely used in most broadcasting systems. However, there are some undesirable artifacts
such as jagged patterns, flickering, and line twitters. Moreover, most recent TV monitors utilize flat panel display
technologies such as LCD or PDP monitors and these monitors require progressive formats. Consequently, the
conversion of interlaced video into progressive video is required in many applications and a number of deinterlacing
methods have been proposed. Recently deinterlacing methods based on neural network have been proposed with good
results. On the other hand, with high resolution video contents such as HDTV, the amount of video data to be processed
is very large. As a result, the processing time and hardware complexity become an important issue. In this paper, we
propose an efficient implementation of neural network deinterlacing using polynomial approximation of the sigmoid
function. Experimental results show that these approximations provide equivalent performance with a considerable
reduction of complexity. This implementation of neural network deinterlacing can be efficiently incorporated in HW
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.
HDTV broadcasting services have become widely available. Furthermore, in the upcoming IPTV services, HDTV
services are important and quality monitoring becomes an issue, particularly in IPTV services. Consequently, there have
been great efforts to develop video quality measurement methods for HDTV. On the other hand, most HDTV programs
will be watched on digital TV monitors which include LCD and PDP TV monitors. In general, the LCD and PDP TV
monitors have different color characteristics and response times. Furthermore, most commercial TV monitors include
post-processing to improve video quality. In this paper, we compare subjective video quality of some commercial HD
TV monitors to investigate the impact of monitor type on perceptual video quality. We used the ACR method as a
subjective testing method. Experimental results show that the correlation coefficients among the HDTV monitors are
reasonable high. However, for some video sequences and impairments, some differences in subjective scores were