應用於影像濃縮之主觀畫質評估技術

隨著科技日新月異，在不同多媒體裝置分享影像/視訊越來越方便、處理速度也越來越快速，這類多媒體裝置的播放螢幕大小不同，所以我們需要對影像/視訊進行濃縮處理，而經過濃縮處理後的影像/視訊，常常會有資訊損失，因此我們需要一套客觀畫質評估方法，量測經過濃縮處理後的影像/視訊畫質好壞。本論文旨在設計一套精確記錄測試者主觀感知的視覺畫質評估方法，藉由此主觀視覺畫質評估方法，我們可以驗證客觀畫質評估演算法的正確性和穩定性，並且得到測試者對於影像/視訊濃縮處理後的各項統計資料。我們的主觀視覺畫質評估方法可以分為三個部分，首先，讓測試者參考原始影像，從兩張經過不同濃縮演算法處理後的影像，挑出主觀視覺畫質較好的一張；接著，讓測試者選擇資訊損失較多的一張影像；最後，詢問測試者，造成主觀視覺畫質不佳的可能原因，包括前景物件變形、幾何結構破壞、視覺比例不佳等原因。依據這三部分的結果，我們計算主觀視覺畫質評估和客觀畫質評估實驗結果的相關性，印證客觀畫質評估演算法是否具有一致性、公平性和穩定度;另外，藉由分析主觀影像資訊流失的統計數據和造成主觀視覺畫質不佳的可能原因，做為改善客觀畫質評估演算法或影像/視訊濃縮演算法的依據。

關鍵字

畫質評估；影像濃縮；視訊濃縮

並列摘要

With the development of various technologies in image/video processing applications, it is more convenient to share images/videos on different multimedia devices. However, due to different display screen sizes of multimedia devices, we have to resize images/videos to deliver the image/video data for users. Since image/video retargeting will cause visual information loss or distortion, an evaluation method to assess the performance of image/video retargeting is much desired. In order to evaluate the visual quality of retargeted images/videos, we design a subjective visual quality assessment method to precisely record human subjective perception. Our subjective visual quality assessment method includes three parts. First, we ask participants to make an overall image subjective visual quality voting. Then, we let participant judge images about the subjective image information loss. Finally, we request participants to choose the subjective reasons for their voting. Through analyzing correlations between the results from the subjective visual quality assessment method and the objective visual quality metric, we verify whether the results from the objective visual quality metric are consistent with those from the subjective visual quality assessment method. Besides, statistical results are used to analyze relations between image attributes, and discuss different types of distortion caused by different image retargeting algorithms.

並列關鍵字

Visual Quality Assessment Method ； Image Retargeting ； Video Retargeting

參考文獻

[1] Z. Wang, H. R. Sheikh, and A. C. Bovik, “Objective video quality assessment,” in The Handbook of Video Database: Design and Applications, B. Furht and O. Marqure, Eds. Boca Raton, FL: CRC Press, Sep. 2003, pp. 1041-1078.

[3] C.-C. Hsu, Y. Fang, C.-W. Lin, and W. Lin, “Quality assessment for image retargeting based on perceptual distortion and information loss,” final report, National Tsing Hua University, Mar. 2012.

[4] S. Avidan and A. Shamir, “Seam carving for content-aware image resizing,” ACM Transactions on Graphics, vol. 26, no. 3 pp. 267-276, Jul. 2007.

[5] M. Rubinstein, A. Shamir, and S. Avidan, “Multi-operator media retargeting,” ACM Transactions on Graphics, vol. 28, no. 3 pp. 1-11, Aug. 2009.

[8] W. Lin and C.-C. Jay Kuo, “Perceptual visual quality metrics: A survey,” J. Vis. Commun. Image R., vol. 22, no. 4 pp. 297-312, May 2011.

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應用於影像濃縮之主觀畫質評估技術

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