Chan-Sik Hwang

Human visual system weighted progressive image transmission using lapped orthogonal transform/classified vector quantization

A progressive image transmission (PIT) scheme based on the classified transform vector quantization (CVQ) technique using the lapped orthogonal transform (LOT) and human visual system (HVS) weighting is proposed. Conventional block transform coding of images using the discrete cosine transform (DCT) produces, in general, undesirable blocking artifacts at low bit rates. Here image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics with HVS weighting to improve the reconstructed image quality by adaptive bit allocation. The subvectors are vector quantized and transmitted progressively. Coding tests using computer simulations show that the LOT/CVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using PIT/DCTVQ. The LOT/CVQ-based PIT reduces the blocking artifact significantly.

Classification of underwater transient signals using MFCC feature vector

This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with mel-frequency cepstral coefficients (MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

Moving cast shadow detection and removal for visual traffic surveillance

Shadow detection and removal is important to deal with traffic image sequences. The shadow cast by a vehicle can lead to inaccurate object feature extraction and an erroneous scene analysis. Furthermore, separate vehicles can be connected through a shadow, thereby confusing an object recognition system. Accordingly, this paper proposes a robust method for detecting and removing an active cast shadow from monocular color image sequences. A background subtraction method is used to extract moving blobs in color and gradient dimensions, and YCrCb color space adopted to detect and remove the cast shadow. Even when shadows link different vehicles, each vehicle figure can be separately detected using a modified mask based on a shadow bar. Experimental results from town scenes demonstrate that the proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

Underwater Transient Signal Classification Using Binary Pattern Image of MFCC and Neural Network

This paper presents a new method for classification of underwater transient signals, which employs a binary image pattern of the mel-frequency cepstral coefficients as a feature vector and a feed-forward neural network as a classifier. The feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the mel-frequency cepstral coefficients that is derived from the frame based cepstral analysis. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

Document Image Layout Analysis Using Image Filters and Constrained Conditions

Document image layout analysis contains the process to segment document image into detailed regions and the process to classify the segmented regions into text, picture, table or etc. In the region classification process, the size of a region, the density of black pixels, and the complexity of pixel distribution are the bases of region classification. But in case of picture, the ranges of these bases are so wide that it`s difficult to decide the classification threshold between picture and others. As a result, the picture has a higher region classification error than others. In this paper, we propose document image layout analysis method which has a better performance for the picture and text region classification than that of previous methods including commercial softwares. In the picture and text region classification, median filter is used in order to reduce the influence of the size of a region, the density of black pixels, and the complexity of pixel distribution. Futhermore the classification error is corrected by the use of region expanding filter and constrained conditions.

Detection of Moving Cast Shadows for Traffic Monitoring System

Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

Error Concealment Method Using Three-Dimensional Motion Estimation

A new block-based error concealment method is proposed that produces non-uniform sized and irregular quadrilateral motion estimation considering three-dimensional motions, such as rotation, magnification, and reduction as well as parallel motion, in moving pictures. The proposed error concealment method uses an affine transform, a type of spatial transform, to estimate the motion of lost block data, then the motion prediction errors are calculated using a weighting matrix and weighted according to the motion vector size for more accurate motion estimation. Experimental results show that the proposed method is able to produce a higher PSNR value and better subjective image quality by decreasing the blocking artifacts.

Error-resilient method for robust video transmissions

In this paper we address the problems of video transmission in error prone environments. A novel error-resilient method is proposed that uses a data embedding scheme for header parameters in video coding standards, such as MPEG-2 and H.263. In case of requiring taking the loss of data information into account except for header errors, the video decoder hides visual degradation as well as possible, employing an error concealment method using an affine transform. Header information is very important because syntax elements, tables, and decoding processes all depend on the values of the header information. Therefore, transmission errors in header information can result in serious visual degradation of the output video and also cause an abnormal decoding process. In the proposed method, the header parameters are embedded into the least significant bits (LSB) of the quantized DCT coefficients. Then, when errors occur in the header field of the compressed bitstream, the decoder can accurately recover the corrupted header parameters if the embedded information is extracted correctly. The error concealment technique employed in this paper uses motion estimation considering actual motions, such as rotation, magnification, reduction, and parallel motion, in moving pictures. Experimental results show that the proposed error-resilient method can effectively reconstruct the original video sequence without any additional bits or modifications to the video coding standard and the error concealment method can produce a higher PSNR value and better subjective video quality, estimating the motion of lost data more accurately.

Error detection and correction using correlation of parametersin MPEG-2 video bitstream

A new error detection and correction method is proposed that relies on the correlations among the syntax parameters of an MPEG-2 bitstream. Since MPEG-2 has led to a variety of applications, the MPEG-2 video specification is quite flexible. The header parameters in a video-coding standard are very important, as the syntax elements, tables, and decoding processes all depend on the values of the header information. Therefore, transmission errors in the header information not only result in a serious visual degradation of the output video, but also cause an abnormal decoding process. A number of error detection and correction techniques have already been developed to recover the MPEG-2 visual quality. However, since most of these methods only consider macroblock data information including quantized DCT coefficients, they are unable to produce good results with videos that include errors in the header information. Accordingly, the current paper proposes a method for detecting and correcting bit errors in headers based on the correlations between header parameters, between consecutive pictures, and between macroblock data and header parameters. As a result, even if bit errors are generated in header parameters, which are crucial to successful decoding, experimental results showed that the proposed header error detection and correction method can improve the video quality without increasing the transmission bit rate.