Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations

Effective detection of traffic participants is crucial for driver assistance systems. Traffic safety data reveal that the majority of preventable pedestrian fatalities occurred at night. The lack of light at night may cause dysfunction of sensors like cameras. This paper proposes an alternative approach to detect traffic participants using cost-effective arrayed ultrasonic sensors. Candidate features were extracted from the collected episodes of pedestrians, cyclists, and vehicles. A conditional likelihood maximization method based on mutual information was employed to select an optimized subset of features from the candidates. The belonging probability to each group along with time was determined based on the accumulated object type attributes outputted from a support vector machine classifier at each time step. Results showed an overall detection accuracy of 86%, with correct detection rate of pedestrians, cyclists and vehicles around 85.7%, 76.7% and 93.1%, respectively. The time needed for detection was about 0.8 s which could be further shortened when the distance between objects and sensors was shorter. The effectiveness of arrayed ultrasonic sensors on objects detection would provide all-around-the-clock assistance in low-speed situations for driving safety

Digital photo album compression and HEVC related issues

In recent years, the advance in computer, IC, internet, cloud, and multimedia technologies have led to widespread usage of digital photos and videos in our everyday multimedia-enabled lives. A consumer can easily have many photo albums each of which contain many JPEG images taken in the same occasion. Typically many photos taken in the same occasion are similar, with similar people and background. With explosive growth of photo albums in the consumers personal computer and in the cloud (such as facebook, twitter, Google, Renren, etc), the cost to store and transmit such digital images can be very significant. Thus it is important to compress these images efficiently. In this thesis, we propose some novel effective photo album compression algorithms to achieve higher compression efficiency than existing methods. Observing that many images in albums are similar, we assume that similar images can be clustered and identified and we develop efficient ways to compress each set of similar JPEG images using video coding techniques. Our approach is to arrange all the similar images into some kind tree structure with many roots and branches and then apply video coding technique along each branch. To maximize the inter-frame correlation between adjacent photos along the branches in a tree, we consider both the minimum spanning tree (MST) and the spanning forest to achieve minimum inter-frame prediction cost. As an unconstrained tree can be very deep which can result in very long delay during random access, we consider trees with limited depth to achieve good random access performance. Taking advantage of the latest High Efficiency Video Coding (HEVC) standard, we consider several possible HEVC frame configuration in search of the best. Our methods can support common photo albums operations such as addition of new images, deletion of useless images and modification of existing images. Experiments suggest that the proposed methods can achieve signficant gain in coding efficiency compared with the common JPEG format. Apart from the above research, this thesis also contains an improvement to HEVC. In the state-of-art video coding standard HEVC, temporal motion vector (MV) predictor is adopted in order to improve coding efficiency. However, motion vector information in reference frames, which is used by temporal MV predictor, takes large amount of bits in memory storage. Therefore motion data needs to be compressed before storing into buffer. Accordingly we propose an adaptive motion data storage reduction method. First, it divides the current 16x16 block in the reference frame into four partitions. One MV is sampled from each partition and all sampled MVs form a MV candidate set. Then it will check if one or two MVs should be stored into the MV buffer by checking the maximum distance between any two of the MVs in the candidate set. If the maximum distance is greater than a certain threshold, the motion data of the two MVs that have maximum distance are put into memory; otherwise the motion data of the upper left block is stored. The basic goal of the proposed method is to improve the accuracy of temporal MV predictor at the same time reducing motion data memory size. Simulation results show that compared to the original HEVC MV memory compression method in the 4th JCT-VC meeting, the proposed scheme achieves a coding gain of 0.5% to 0.6%; and the memory size is reduced by more than 87.5% comparing to without using motion data compression

Similar Images Compression based on DCT Pyramid Multi-level Low Frequency Template

Medical imaging applications produce a huge amount of similar images. Instead of compressing each image individually, set redundancy compression (SRC) methods remove the inter image redundancy and reduce storage. However, in the previous SRC methods-MMD, MMP and Centroid methods, the prediction templates for extracting set redundancy are not very efficient, especially when image sets are very large with several clusters. In this paper, inspired by face recognition techniques, a novel lossless SRC method is derived based onDCT pyramid multi-level low frequency template. The approximation subband is used as a prediction template for each image to calculate the residue. Intra prediction is also used to reduce the entropy of the residues. Experiments with 3 sets of MR brain images demonstrate the efficiency of our proposed algorithm in respect to bits/pixel (bpp)

An adaptive unsupervised approach toward pixel clustering and color image segmentation

This paper proposes an adaptive unsupervised scheme that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation The algorithm, named Ant Colony-Fuzzy C-means Hybrid Algorithm (AFHA), adaptively clusters image pixels viewed as three dimensional data pieces in the RGB color space The Ant System (AS) algorithm is applied for intelligent initialization of cluster centroids. which endows clustering with adaptivity. Considering algorithmic efficiency, an ant subsampling step is performed to reduce computational complexity while keeping the clustering performance close to original one. Experimental results have demonstrated AFHA clustering's advantage of smaller distortion and more balanced cluster centroid distribution over FCM with random and uniform initialization Quantitative comparisons with the X-means algorithm also show that AFHA makes a better pre-segmentation scheme over X-means We further extend its application to natural image segmentation. taking into account the spatial information and conducting merging steps in the image space Extensive tests were taken to examine the performance of the proposed scheme Results indicate that compared with classical segmentation algorithms such as mean shift and normalized cut, our method could generate reasonably good or better image partitioning, which illustrates the method's practical value (C) 2009 Elsevier Ltd. All rights reserve

An adaptive motion data storage reduction method for temporal predictor

In the state-of-art video coding standard HEVC, temporal motion vector (MV) predictor is adopted in order to improve coding efficiency. However, motion vector information in reference frames, which is used by temporal MV predictor, takes significant amount of bits in memory storage. Therefore motion data needs to be compressed before storing into buffer. In this paper we propose an adaptive motion data storage reduction method. First, it divides the current 16x16 block in the reference frame into four partitions. One MV is sampled from each partition and all sampled MVs form a MV candidate set. Then it judges if one or two MVs should be stored into the MV buffer by checking the maximum distance between any two of the MVs in the candidate set. If the maximum distance is greater than a certain threshold, the motion data of the two MVs that have maximum distance are put into memory; otherwise the motion data of the upper left block is stored. The basic goal of the proposed method is to improve the accuracy of temporal MV predictor at the same time reducing motion data memory size. Simulation results show that compared to the original HEVC MV memory compression method in the 4th JCT-VC meeting, the proposed scheme achieves a coding gain of 0.5%~0.6%; and the memory size is reduced by more than 87.5% comparing to without using motion data compression. © 2011 Springer-Verlag

Digital photo album compression based on Global Motion Compensation and Intra/Inter prediction

With the increasing popularity of digital camera, organizing and managing the large collection of digital photos effectively become popular. Most researchers focus on clustering and image retrieval, but never on photo album compression. Some set redundancy methods for compressing similar image sets cannot handle photo album compression well because they don't account for any camera and object motion information between images. In this paper, we analyzed the characteristics of camera and object motion between similar photos and designed a novel lossy coding scheme for similar image sets. In our proposed coding scheme, we applied Global Motion Compensation (GMC), Local Motion Compensation (LMC) and Intra prediction inspired from video coding technology. Our method provides a compact and reasonable format for people to store and transmit their large number of digital photos. Experiments prove that our algorithm is efficient and effective for digital photo processing. © 2012 IEEE

Fast Sub-Pixel Motion Estimation with Simplified Modeling in HEVC

Motion estimation (ME) is one of the key elements in video coding standard which eliminates the temporal redundancies between successive frames. In recent international video coding standards, sub-pixel ME is proposed for its excellent coding performance. Compared with integer-pixel ME, sub-pixel ME needs interpolation to get the value in sub-pixel position. Also, Hadamard transform will be applied in order to achieve better performance. Therefore, it is becoming more and more critical to develop fast sub-pixel ME algorithms. In this paper, a novel fast sub-pixel ME algorithm is proposed which makes full use of 8 neighboring integer-pixel points. This algorithm models the error surface in sub-pixel position by a second order function with five parameters two times to predict the best sub-pixel position. Experimental results show that the proposed method can reduce the complexity significantly with negligible quality degradation

Modified distortion redistribution problem for High Efficiency Video Coding(HEVC)

Adaptive quantization matrix design for different block sizes is one of the possible methods to improve the RD performance in video coding and has recently attracted the focus of many researchers. In this paper, we first analyze the shortcomings of the evenly distributed distortion method which was proposed recently. In order to tackle these problems, we propose two modified methods, method I with relaxed distortion constraints and method II is iterative boundary distortion minimization problem considering variance adaptively. Both problems can be solved using convex optimization effectively and efficiently. Simulations have been conducted based on HM4.0, which is the reference software of the latest High Efficiency Video Coding (HEVC). Simulation results show the effect of our proposed methods. Both methods show their significance when evaluated by RD performance

An improved method for color images enhancement considering HVS

Viewing the image produces a subjective experience that corresponds with viewing the real scene. The preservation or enhancement of brightness, contrast, and color is very important for color image processing. While most previous algorithms are focusing on the luminance component, this paper presents an algorithm for color enhancement focusing on chromatic components in the compressed domain. On the basis of DC-T transform, the proposed algorithm provides different treatments on different image blocks considering the perceptual properties of Human Visual System (HVS). A simple method in reducing blocking artifacts, which is based on Weber-Fechner Law is introduced. This paper also compares the results of experiments and discusses the effects of different parameters. Experiments indicate that through choosing optimal parameter the algorithm performs better in color enhancement and blocking artifacts reduction compared to other compressed domain based approaches, such as alpha rooting. © 2012 IEEE

ADAPTIVE SEARCH RANGE ALGORITHM BASED ON CAUCHY DISTRIBUTION

In video coding standard, motion estimation (ME) always plays an important role in reducing temporal redundancies at the expense of higher computational complexity. Many fast ME algorithms have been proposed to reduce the coding complexity. Some papers focus on applying specific search patterns to reduce the search points within a fixed search range (SR). But there are only a few of them trying to reduce the size of SR. In this paper, an adaptive SR algorithm is presented. Cauchy distribution is used to model the SR for one frame and the information of motion vector differences in the neighboring blocks is used to adjust the SR for a particular block. Experimental results show that the proposed algorithm can reduce the size of SR significantly with negligible quality degradation