Browsing by Author "WEN, KA"

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A NEW TRANSFORM ALGORITHM FOR VITERBI DECODING
(1990-06-01)
WEN, KA
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WEN, TS
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WANG, JF
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
HYBRID VECTOR QUANTIZATION
(1993-07-01)
WEN, KA
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LU, CY
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交大名義發表
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電子工程學系及電子研究所
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National Chiao Tung University
Vector quantization (VQ) is a powerful technique for low-bit-rate image coding. However, initial studies of image coding with VQ have revealed that VQ causes degradations, most notably around edges. Moreover, the computational complexity is high. Although a few algorithms have been developed to reduce edge degradation, such as block truncation coding (BTC) with VQ (BTC/VQ) or classified vector quantization, their compression ratios are not satisfactory. Discrete cosine transformation with VQ (DCT/VQ) has been applied to image compression, showing a high compression ratio, but the edge degradation problem still exists. We present an image compression algorithm that takes advantage of the merits of DCT/VQ and BTC/VQ to achieve a high-quality and low-bit-rate compression of images. High quality images can be achieved at rates of 0.34 to 0.46 bit/pixel.
THE TRANSFORM IMAGE CODEC BASED ON FUZZY CONTROL AND HUMAN VISUAL-SYSTEM
(1995-08-01)
WEN, KA
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LU, CY
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TSAI, MC
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交大名義發表
;
電子工程學系及電子研究所
A novel processing scheme for gray level image compression based on the human visual system (HVS) and fuzzy control is proposed. The spatial model of the threshold vision that incorporates the masking processes takes account of two major sensitivities of the human visual system, namely background illumination levels and spatial frequency sensitivities. The distortion measures use common sense fuzzy rules for image quality prediction. The human visual models have been successfully applied in image compression. By the addition of the visual model, performance of these systems have a visible improvement of subject quality depending on visual perception. The processed image performs both improved compression ratio as well as improved SNR (signal to noise ratio) in compression to the standardized still image compression technique, i.e., the JPEG.