A Study of Multimedia Compression and Vision-Based Analysis Techniques for Embedded Platform Development and Intelligent Transportation System Applications

• Main Authors: Huang, Hao-Yu, 黃皓昱
• Other Authors: Wu, Bing-Fei
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/74194936742247974023

博士 === 國立交通大學 === 電控工程研究所 === 100 === Due to the recent advances in vehicle technology, the Intelligent Transportation System (ITS) has become one of the important issues in the current studies. Among the researches of ITS, voice recording, real-time event data recorder, and Blind Spot Detection (BSD) techniques become more and more significant in the current days. For the case of voice recording, audio compression technique makes the voice recording much effective, Among the audio compression standards, Advanced Audio Coding (AAC) provides CD audio quality at a 96Kbps bit rate and has become the audio compression standard in the recent years. With the limited Internet bandwidth and portable device storage, AAC now leads audio compression technology. Besides, the image compression technique is also substantial for the event data recording. However, the image and video coding standards do not fit the requirements of embedded applications for the low complexity and low memory consumption. In accordance with the application of the event data recording, it is necessary to implement a low-complexity and low-memory image coder. Another critical issue in the ITS is the study of the BSD. BSD by applying the image processing and computer vision technology becomes a popular topic in the studies of ITS. In this dissertation, we will present several algorithmic, practical, and integrated methods and systems for the above-mentioned applications. Additionally, these applications are implemented on the embedded platform. Chapter 2 presents several optimization approaches for the MPEG-2/4 AAC Low Complexity (LC) encoding and decoding processes. This study focuses on optimizing the Temporal Noise Shaping (TNS), Mid/Side (M/S) Stereo, Modified Discrete Cosine Transform (MDCT) and Inverse Quantization (IQ) modules in the encoder and decoder. Furthermore, we also propose an efficient memory reduction approach that provides a satisfactory balance between the reduction of memory usage and the expansion of the encoded files. Experimental results demonstrate that the proposed AAC codec is computationally effective, has low memory consumption, and is suitable for low-cost embedded and mobile applications. In Chapter 3, we propose a block-edge-based Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC) method. SPPEZC combines two novel compression concepts, called Block-Edge Detection (BED) and the Low-Complexity and Low-Memory Entropy Coder (LLEC), for coding efficiency and quality. Based on the block-edge information, this paper proposes an adaptive architecture for adjusting the quantization table and subsequently coding the quantized coefficients with the LLEC. Experimental results and comparisons demonstrate that the proposed SPPEZC technique provides computational efficiency as well as satisfactory perceptual quality in compressed images. In Chapter 4, we present an effective Blind Spot Warning System (BSWS) for daytime and nighttime conditions. The proposed BSWS includes camera models of a dynamic calibration, the Region of Interest (ROI) initialization and updating, and blind spot detection (BSD) algorithms for the daytime and nighttime. Under daytime conditions, the proposed system presents the Horizontal Edge and Shadow Composite Region (HESCR) method to extract the searching region and to acquire the shadow location of the targeted vehicles. Additionally, to detect obstacles and vehicles at nighttime road scenes, the proposed system extracts bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. Experimental results show that the proposed BSD system is feasible for vehicle detection and collision warning in various daytime and nighttime road environments. Finally, we give a brief conclusion and future works in Chapter 5.