ÄãºÃ£¬»¶Ó­À´µ½¾­¹ÜÖ®¼Ò [µÇ¼] [×¢²á]

ÉèΪÊ×Ò³ | ¾­¹ÜÖ®¼ÒÊ×Ò³ | Êղر¾Õ¾

»ùÓÚС²¨±ä»»µÄÊý¾ÝѹËõËã·¨µÄÑо¿ÓëʵÏÖ_ͨÐŹ¤³ÌרҵÂÛÎÄ

·¢²¼Ê±¼ä£º2015-01-24 À´Ô´£ºÈË´ó¾­¼ÃÂÛ̳
ͨÐŹ¤³ÌרҵÂÛÎÄ Ä¿Â¼ ÕªÒª III ABSTRACT IV µÚÒ»Õ Ð÷ÂÛ 1 1.1 ¿ÎÌâÑо¿µÄ±³¾° 1 1.2 ¿ÎÌâÑо¿µÄÈÎÎñºÍÄ¿±ê 1 1.3 ÄÚÈÝ°²ÅÅ 2 µÚ¶þÕ С²¨±ä»»Í¼ÏñѹËõ¼¼Êõ»ù´¡ 3 2.1 С²¨±ä»»¼ò½é 3 2.2 С²¨±ä»»»ù±¾ÀíÂÛ 4 2.2.1 Á¬ÐøС²¨±ä»» 4 2.2.2 ÀëɢС²¨±ä»» 7 2.3 ¶à·Ö±æÂÊ·ÖÎö 12 2.4 ¼¸ÖÖ³£¼ûµÄС²¨ 13 2.5 С²¨±ä»»ÓëͼÏñѹËõ 15 2.6 ´ÓÄÜÁ¿½Ç¶È¿´Ð¡²¨±ä»» 16 µÚÈýÕ »ùÓÚС²¨±ä»»µÄͼÏñѹËõËã·¨µÄÑо¿ 18 3.1 ¼¸ÖÖС²¨Í¼ÏñѹËõËã·¨µÄ½éÉÜ 18 3.1.1 ¼¸ÖÖС²¨Í¼ÏñѹËõËã·¨µÄ½éÉÜ 18 3.1.2 ¹ØÓÚJPEG2000 20 3.2 ¸÷ÖÖС²¨Í¼ÏñѹËõËã·¨µÄ¹²ÐÔ 20 3.3 ¶Ô¸÷ÖÖС²¨Ñ¹ËõËã·¨µÄ±È½Ï¼°±¾¿ÎÌâËùÑ¡·½°¸ 21 3.3.1 ¶Ô¸÷ÖÖС²¨Ñ¹ËõËã·¨µÄ±È½Ï 21 3.3.2 ±¾¿ÎÌâËùÑ¡·½°¸ 21 3.4 ËùÑ¡·½°¸µÄʵÏÖ 21 3.4.1 EZWËã·¨¸ÅÊö 21 3.4.2 ÁãÊ÷µÄ¹¹Ôì 22 3.4.3 ɨÃè·½·¨ 23 3.4.4 EZWËã·¨µÄʵÏÖ 23 3.5 EZWËã·¨¾ÙÀý 24 3.5.1 Ê÷½á¹¹ 24 3.5.2 ±àÂë 25 3.5.3 ½âÂë¹ý³Ì 27 3.6 ¹ØÓÚ±àÂë 28 µÚËÄÕ »ùÓÚС²¨±ä»»µÄͼÏñѹËõËã·¨µÄÈí¼þʵÏÖ 29 4.1 Èí¼þ×ÜÌ幦ÄÜÉè¼Æ 29 4.2 Èí¼þ¸÷¸öÄ£¿éµÄʵÏÖ 30 4.3 Èí¼þÓ¦ÓþÙÀý 35 4.4 Èí¼þÆÀ¼Û 37 4.5 ±¾Èí¼þϵͳµÄÓŵãºÍ²»×ã 38 4.6 ÖµµÃ¸Ä½øµÄµØ·½ 38 µÚÎåÕ ×ܽá 39 5.1 Ëù×ö¹¤×÷µÄ×ܽá 39 5.2Õ¹Íû 39 ²Î¿¼ÎÄÏ× 41 ÖÂл 43 ¸½Â¼ 44 ÕªÒª ÐÅϢʱ´úµÄµ½À´Ê¹ÈËÃǼ«Ò×»ñµÃ´óÁ¿µÄÐÅÏ¢£¬Êý×ÖͼÏñ¾ÍÊÇÒ»ÖÖÖØÒªµÄÐÅÏ¢ÔØÌå¡£ÈçºÎ´æ´¢ºÍ´«ÊäÕâЩͼÏñÒ»Ö±ÊÇÈËÃǹØ×¢µÄ½¹µã£¬¶Ô´Ë£¬ÈËÃÇÒ²Ìá³öÁËÐí¶à·½·¨ºÍÖƶ¨ÁËÐí¶à±ê×¼¡£Ð¡²¨±ä»»×÷ΪһÃŽÏеÄÊýѧ·ÖÖ§£¬±»ÒýÈëͼÏñ´¦ÀíÒԺ󣬺ܿìÒýÆðÁËÈËÃǵļ«´óÐËȤ¡£Ëæ×ÅÑо¿µÄ¿ªÕ¹£¬Ïà¼Ì³öÏÖÁËÐí¶à»ùÓÚС²¨±ä»»µÄͼÏñѹËõ·½Ê½£¬ÈçǶÈëʽÁãÊ÷Ëã·¨£¨EZW£¬the Embedded Zerotree Wavelet algorithm £©¡¢·Ö²ãÊ÷¼¯ºÏ·Ö¸îËã·¨£¨SPIHT£¬Set Partitioning In Hierarchical Trees£©¡¢×î¼Ñ½Ø¶ÏǶÈëÂë¿éËã·¨ ( EBCOT £¬Embedded Block Coding with Optimized Truncation)µÈµÈ£¬ÔÚ´Ë»ù´¡ÉÏ£¬ÈËÃÇ»¹Öƶ¨ÁË»ùÓÚС²¨±ä»»µÄ¹ú¼Ê»¯Í¼ÏñѹËõ±ê×¼JPEG2000¡£ ±¾ÎÄÊ×ÏȽéÉÜÁËС²¨±ä»»ÀíÂ۵ķ¢Õ¹Çé¿öºÍһЩ»ù±¾µÄС²¨ÀíÂÛ£¬È»ºóÓÖ½éÉÜÁ˼¸ÖÖ»ùÓÚС²¨±ä»»µÄͼÏñѹËõ¼¼Êõ²¢¶ÔËüÃÇ×öÁ˼òµ¥µÄ±È½Ï£¬½ô½Ó×ÅÖصãÂÛÊöÁËEZWËã·¨µÄÔ­ÀíºÍʵÏÖ£¬ÕâÒ²ÊDZ¾ÎĵÄÖص㡣×îºó£¬ÊµÏÖÁË»ùÓÚEZWËã·¨µÄÒ»¸öÈí¼þϵͳ¡£ ¹Ø¼ü´Ê£º ͼÏñѹËõ£¬Ð¡²¨£¬ÀëɢС²¨±ä»»£¬EZWËã·¨ ABSTRACT People can easily obtain a large amount of information with the arrival of the information age, digital image is an important information carrier. How to store and transmit these digital images has been the focus of people's attention, thus, many methods were proposed and a number of standards were shaped. As a relatively new branch of mathematics, wavelet transform has been introduced into image processing, and quickly caught the great interest. Following this study, many image compression methods which based on wavelet transform have been launched, for example, the Embedded Zerotree Wavelet algorithm (EZW), Set Partitioning In Hierarchical Trees (SPIHT), Embedded Block Coding with Optimized Truncation (EBCOT), and so on. On these basses, it has developed international image compression standard JPEG2000 based on wavelet transform. Firstly, this paper introduces the basic wavelet transform theory and the development of the wavelet theory, then introduces several image compression technology based on wavelet transform , compares them Simply, and treatises the EZW algorithm and its implementation, which is the focus of this paper. Finally, it¡¯s realized a software system based on the EZW algorithm. KEY WORDS: Image compressing, Wavelets, Discrete Wavelet Transform, EZW algorithm
¾­¹ÜÖ®¼Ò¡°Ñ§µÀ»á¡±Ð¡³ÌÐò
  • ɨÂë¼ÓÈë¡°¿¼ÑÐѧϰ±Ê¼ÇȺ¡±
ÍƼöÔĶÁ
¾­¼ÃѧÏà¹ØÎÄÕÂ
±êÇ©ÔÆ
¾­¹ÜÖ®¼Ò¾«²ÊÎÄÕÂÍƼö