A Predictive Classifier for Image Vector Quantization

• Main Authors: I-Sheng Kuo, 郭萓聖
• Other Authors: Shen-Chuan Tai
• Format: Others
• Language: en_US
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/51311548448930956892

碩士 === 國立成功大學 === 電機工程學系 === 87 === In this Thesis, a new scheme of still image compressor using vector quantization is proposed. A new classification method of edge blocks and a new prediction method for both classification types and VQ indices are proposed for our new encoder. To achieve better performance, the encoder decomposes images into smooth and edge areas, and encodes them separately using different algorithms. MRVQ with block size 8’8 and 16’16 pixels is applied to smooth areas to achieve higher compression ratio. A total of 32 predicted-CVQ types are applied to the edge areas to achieve good quality. The proposed prediction method has accuracy of about 50% when applying to edge areas only. By applying the proposed encoding scheme to still image 'Lena', the bitrate of 0.219bpp with the PSNR of 30.59dB is achieved.