適用於數位家庭媒體整合之JPEG-LS演算法以區段為基準的流量控制方法設計及分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：7

、訪客IP：18.216.233.58

姓名

高淑卿(Shu-Ching Kao) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

適用於數位家庭媒體整合之JPEG-LS演算法以區段為基準的流量控制方法設計及分析
(Design and Analysis of JPEG-LS algorithm with efficient segment-based rate control scheme for digital home media integration)

相關論文

★ 即時的SIFT特徵點擷取之低記憶體硬體設計	★ 即時的人臉偵測與人臉辨識之門禁系統
★ 具即時自動跟隨功能之自走車	★ 應用於多導程心電訊號之無損壓縮演算法與實現
★ 離線自定義語音語者喚醒詞系統與嵌入式開發實現	★ 晶圓圖缺陷分類與嵌入式系統實現
★ 語音密集連接卷積網路應用於小尺寸關鍵詞偵測	★ G2LGAN: 對不平衡資料集進行資料擴增應用於晶圓圖缺陷分類
★ 補償無乘法數位濾波器有限精準度之演算法設計技巧	★ 可規劃式維特比解碼器之設計與實現
★ 以擴展基本角度CORDIC為基礎之低成本向量旋轉器矽智產設計	★ JPEG2000靜態影像編碼系統之分析與架構設計
★ 適用於通訊系統之低功率渦輪碼解碼器	★ 應用於多媒體通訊之平台式設計
★ 適用MPEG 編碼器之數位浮水印系統設計與實現	★ 適用於視訊錯誤隱藏之演算法開發及其資料重複使用考量

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著液晶顯示器 (LCD) 技術的發展，高速、高深度色彩及高解析度的需求都是不可或缺的。目前已發展出很多高速數位傳輸標準，例如高清晰度多媒體接口 (HDMI) 及無線-HD (wireless-HD)。由於一些物理性的限制，所以很多傳輸標準並不適用於數位家庭多媒體整合 (DHMI)。因此，在有限的記憶體及傳輸頻寬下，利用有提供比率控制技術的近似無失真壓縮演算法來達到其要求。對影像壓縮而言，壓縮率及高影像品質是很基本的需求。在數位家庭多媒體的應用上，為了使數位資料能有效地傳輸並節省硬體傳輸成本，所以我們整合了以列為基準的流量控制方法於JPEG-LS。
在本文中，為了實現低成本的DHMI，因而提出了一個有效且可行的解決方法，即在JPEG-LS上加入了以區段為基準的流量控制 (SBRC) 方法。 SBRC利用人眼感知特性及局部材質分析來最佳化信息損耗分配及達到最好的比率控制。因此，根據人眼感知的特性，我們提出來可支援縮放比例的JND模組來最佳化重建影像的品質。
實驗結果顯示，利用我們方法所得的模擬結果與沒有流量控制方法的JPEG-LS結果作比較，只降低了少數的PSNR。並且根據我們所提出的方法，可使多個媒體來源的數位資料只需透過單一的HDMI電纜就可傳輸至所對應的播放媒體。

摘要(英)

With the advancement of liquid crystal display (LCD) technology, high speed, high-color-depth and high-resolution is indispensable. Many high speed digital transmission standards have been developed, such as High Definition Multimedia Interface (HDMI) and wireless-HD. Due to physical limitations, many transmission standards are not suitable for Digital Home Media Integration (DHMI). Moreover, in recent years, the near-lossless compression algorithm with rate control scheme is applied for limited memory storage and transmission bandwidth. For image compression, both compression ratio and high visual quality become primary requirements for high-end applications. In order to make the digital data to be transmitted efficiently and save the cost, we integrate row-level rate control scheme with the JPEG-LS for digital home media applications.
In this thesis, an efficient and feasible solution with the segment-based rate control (SBRC) scheme in JPEG-LS for low cost DHMI is proposed. The SBRC scheme exploits the characteristics of human visual perception and local texture analysis to optimize the information loss distribution and achieves the best rate control. Furthermore, for human visual perception, the scaling JND model is developed to optimize the reconstructed image quality.
Experimental results are reported to support the performance of the proposed scheme. With our method, the experiment results show that only minor PSNR is degraded in comparison with non-rate controlled JPEG-LS and the digital data can be transmitted from multiple source providers to the corresponding display media through a single HDMI cable.

關鍵字(中)

★ 流量控制
★ JPEG-LS

關鍵字(英)

★ rate control
★ JPEG-LS

論文目次

Abstract…………………………………………………………………………... i
List of Figures…………………………………………………………………..... iii
List of Tables……………………………………………………………………... v
Chapter 1 Introduction………………………………………………………... 1
1.1 Motivation…………………………………………………………...... 1
1.2 Thesis Organization…………………………………………………... 5
Chapter 2 Background………………………………………………………... 6
2.1 Fundamental Concepts of JPEG-LS system…………………………. 7
2.1.1. Context Based Statistical Modeling…………………………… 8
2.1.2. Non-Linear Prediction………………………………………… 10
2.1.3. Golome-Rice Coding………………………………………….. 12
2.1.4. Run-Length Coding……………………………………………. 13
2.2 High Definition Multimedia Interface (HDMI)……………………… 14
2.3 Rate Control Scheme………………………………………………… 16
2.4 JND Model…………………………………………………………… 17
Chapter 3 The Proposed Rate Control Scheme in JPEG-LS……………….. 19
3.1 The Adopted Principle of Perceptual-based Information Loss………. 20
3.2 The Mechanism of Segment Based Rate Control Scheme…………... 22
3.3 The Progressive Adjustment of Information Loss Level in SBRC
Scheme………………………………………………………………...
24
Chapter 4 Experiment Result and Analysis………………………………….. 33
4.1 Experiment Environment…………………………………………….. 34
4.2 The Comparison of Objective Image Performance…………………... 36
4.3 The Comparison of Subjective Image Performance…………………. 41
Chapter 5 Conclusions………………………………………………………… 46
References………………………………………………………………………... 48
Appendix I……………………………………………………………………….. 52

參考文獻

[1] ISO/IEC JTC 1/SC 29/WG1 FCD 14495-public draft, July 16th, 1997. http://www.jpeg.org/public/jpeglinks.htm.
[2] M. J. Weinberger, J. Rissanen, and R. Arps, "Applications of universal context modeling to lossless compression of gray-scale images," Journal, IEEE Trans. Image Processing, vol. 5, pp. 575-586, Apr. 1996.
[3] J. Jiang and C. Grecos, “A low cost design of rate controlled JPEG-LS near lossless image compression,” Journal, Image and Vision Computing 19, pp. 153-164, 2001.
[4] Jianmin Jiang, “A Low-Cost Content-Adaptive and Rate-Controllable Near-Lossless Image Codec in DPCM Domain,” IEEE Trans. on image processing, vol. 9, No. 4, April 2000.
[5] E.A. Edirisinghe and S. Bedi, “Variation of JPEG-LS to low cost rate control and its application in region-of-interest based coding,” Journal, Image and Vision Computing 18, pp. 357-372, 2003.
[6] J. Jiang and S. Y. Yang, “ A Rate-Controlled Near Lossless Image CODEC Based on Visual Perception and Content Adaptability,” Proceedings of SPIE, multimedia systems and applications III, vol. 4209, 2001
[7] C. Grecos and J. Jiang, “Achieving a better balance between image compression and image quality,” IEE Electron. Lett. 35, pp.2019-2020, Nov. 1999.
[8] J. Jiang and M. Reddy, “Open-loop rate control for JPEG-LS near lossless image compression,” IEE Electron. Lett. , vol. 35, March 1999.
[9] L. J. Lin and A. Ortega, “Bit-rate control using piecewise approximated rate-distortion characteristics,” IEEE Trans. Circuit Syst. Video Technol., vol. 8, pp. 446-459, Aug. 1998.
[10] The digitimes Website. [online] http://tech.digitimes.com.tw/ShowNews.aspx
?zCatId=416&zNotesDocId=0000041629_A312WN52SW39VQ98XU8XP.htm.
[11] The HDMI Website. [online] http://www.hdmi.org/manufacturer/technology. asp
[12] Collins. L., “The big hook up - The consumer electronics industry has found its success is all about connections,” electronics systems and software, vol. 5, pp.12-15, Feb. 2007
[13] Krishnamoorthy, Rajeev, “High Definition, Anywhere: How Ultra Wideband Makes Wireless HDMI Possible,” IEEE consumer communications and networking conference, pp.395-399, Jan. 2007.
[14] M.J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A low complexity, context-based, lossless image compression algorithm,” in Proc. 1996 Data Compression Conference, Snowbird, UT, Mar. 1996, pp. 140-149.
[15] Information Technology – Lossless and Near-Lossless Compression of Continuous-Tone Still Images, 1999. ISO/IEC 14495-1, ITU Recommend. T.87.
[16] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Trans. on Image Processing, vol. 9, No. 8, Aug. 2000.
[17] ---, “Opitmal prefix codes for sources with two-sided geometric distributions,” IEEE Trans. Inform. Theory, vol. 46, pp.121-135, Jan. 2000.
[18] N. Merhav, G. Seroussi, and M. J. Weinberger, “Coding of sources with two-sided geometric distributions and unknown parameters,” IEEE Trans. Inform. Theory, vol. 46,pp. 229-236, Jan. 2000.
[19] G. G. Langdon Jr., “An adaptive run-length coding algorithm,” IBM Tech. Disclosure Bull., vol. 26, pp. 3783-3785, Dec. 1983.
[20] S. W. Golomb, “Run-length encodings,” IEEE Trans. Inform. Theory, vol. IT-12, pp.399-401, July 1966.
[21] N. S. Jayant, J. D. Johnston, and R. J. Safranek, “Signal compression based on models of human perception,” IEEE Proc. Vol. 81, pp. 1385-1422, 1993.
[22] C. H. Chou and Y. C. Li, “A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile,” Journal, IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 467-476, Dec. 1995.
[23] I. Höntsch and L. J. Karam, “Locally adaptive perceptual image coding,” Journal, IEEE Trans. Image Processing, vol. 9, pp. 1472-1483, Sept. 2000.
[24] R. J. Safranek, “A comparison of the coding efficiency of perceptual models,” in Proc. SPIE, vol. 2411, pp. 83-91, 1995.
[25] K. H. Yang, W. Zhu, and A. F. Faryar, “Perceptual Quantization for Predictive Coding of Images,” IEEE Trans. Image Processing, vol. 2, pp.381-385, Oct. 1999.
[26] X. K. Yang, W. S. Lin, Z. Lu, E. P. Ong, and S Yao, “Just-noticeable-distortion profile with nonlinear additivity model for perceptual masking in color images,” IEEE Conf. on Acoustics, Speech, and Signal Processing, Vol. 3, April 2003.
[27] Chun-Hsien Chou; Kuo-Cheng Liu, “A visual model for estimating the perceptual redundancy inherent in color images,” On Intelligent Multimedia, Video and Speech Processing, pp. 530-533, Oct. 2004
[28] Shan Suthaharan, Seong-Whan Kim, and K. R. Rao, “A new quality metric based on just-noticeable difference, perceptual regions, edge extraction and human vision,” Electrical and Computer Engineering, vol. 30, pp. 81-88, 2005.

指導教授

蔡宗漢(Tsung-Han Tsai)

審核日期

2007-12-24

推文