计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
19期
13-23,151
,共12页
张淑芳%张聪%张涛%雷志春
張淑芳%張聰%張濤%雷誌春
장숙방%장총%장도%뢰지춘
图像质量评价%无参考%通用型%特征提取
圖像質量評價%無參攷%通用型%特徵提取
도상질량평개%무삼고%통용형%특정제취
image quality assessment%no reference%universal%feature extraction
图像质量评价可有效评估图像采集和传输过程引起的失真或退化，在数字多媒体领域具有广阔的应用前景，无参考图像质量评价算法由于不需要参考图像先验知识，近年来成为图像质量评价领域研究的热点。在对国内外文献进行广泛调研的基础上，从评价算法原理和性能比较两个方面，系统综述了BIQI、DIIVINE、BLIINDS、BLIINDS-II、BRISQUE、NIQE和GRNN等当前性能较优的几种无参考图像质量评价算法。介绍了各种算法的特征提取和质量评价原理，在LIVE数据库上对上述评价方法进行仿真评估，并分析和比较了各种算法的评价性能和执行速度，提出了无参考评价方法的进一步研究方向。综述的几种无参考图像质量评价算法虽然已具有很好的效果，但在评价时严重依赖数据库中的主观评价数据，并且在评价精度和算法复杂度方面还存在一些不足，需要进行深入研究。
圖像質量評價可有效評估圖像採集和傳輸過程引起的失真或退化，在數字多媒體領域具有廣闊的應用前景，無參攷圖像質量評價算法由于不需要參攷圖像先驗知識，近年來成為圖像質量評價領域研究的熱點。在對國內外文獻進行廣汎調研的基礎上，從評價算法原理和性能比較兩箇方麵，繫統綜述瞭BIQI、DIIVINE、BLIINDS、BLIINDS-II、BRISQUE、NIQE和GRNN等噹前性能較優的幾種無參攷圖像質量評價算法。介紹瞭各種算法的特徵提取和質量評價原理，在LIVE數據庫上對上述評價方法進行倣真評估，併分析和比較瞭各種算法的評價性能和執行速度，提齣瞭無參攷評價方法的進一步研究方嚮。綜述的幾種無參攷圖像質量評價算法雖然已具有很好的效果，但在評價時嚴重依賴數據庫中的主觀評價數據，併且在評價精度和算法複雜度方麵還存在一些不足，需要進行深入研究。
도상질량평개가유효평고도상채집화전수과정인기적실진혹퇴화，재수자다매체영역구유엄활적응용전경，무삼고도상질량평개산법유우불수요삼고도상선험지식，근년래성위도상질량평개영역연구적열점。재대국내외문헌진행엄범조연적기출상，종평개산법원리화성능비교량개방면，계통종술료BIQI、DIIVINE、BLIINDS、BLIINDS-II、BRISQUE、NIQE화GRNN등당전성능교우적궤충무삼고도상질량평개산법。개소료각충산법적특정제취화질량평개원리，재LIVE수거고상대상술평개방법진행방진평고，병분석화비교료각충산법적평개성능화집행속도，제출료무삼고평개방법적진일보연구방향。종술적궤충무삼고도상질량평개산법수연이구유흔호적효과，단재평개시엄중의뢰수거고중적주관평개수거，병차재평개정도화산법복잡도방면환존재일사불족，수요진행심입연구。
Image quality assessment can effectively evaluate distortion or degradation caused by image acquisition and transmission process, which has a broad application prospect in the field of digital multimedia. And because of no need any pristine knowledge of reference images, no-reference image quality assessment has become an advanced research hot-spot in the field of image quality assessment. On the basis of extensive research of literatures at home and abroad, in both of algorithm principle and performance comparison, this paper systematically introduces several state-of-the-art no-refer-ence IQA algorithms, such as BIQI, DIIVINE, BLIINDS, BLIINDS-II, BRISQUE, NIQE and GRNN. Firstly, the methods of feature extraction and the principle of quality assessment of each algorithm are introduced. Secondly, the algorithms above are simulated and evaluated on the LIVE image database, and the performance and execution speed of the algo-rithms are analyzed and compared. At last, the further research trends of no-reference image quality assessment are pro-posed. Although these no-reference image quality assessments reviewed in this paper have satisfactory performance, their processes of evaluating image quality heavily depend on opinion data of image quality in the image database, and there still exist some deficiencies in evaluation performance and algorithm complexity. Therefore, it is necessary to make fur-ther study in this field.
