Browsing by Subject "Image quality assessment"
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Item Open Access Bulanıklık tespiti birikimli olasılığına dayalı kızılötesi kamera otomatik odaklanması(IEEE, 2014-04) Çakır, Serdar; Çetin, A. EnisNesne iz ölçümü ve analizinde kızılötesi (KÖ) kameralar önemli bir rol oynamaktadır. Özellikle araştırma ve askeri amaçlı kullanılan bilimsel KÖ kameralarda odaklama el ile yapılmakta ve bu durum alınan ölçümün hassasiyet ve güvenilirliğini azaltmaktadır. Otomatik kamera odaklama algoritmaları imgeden çeşitli öznitelikler çıkararak en iyi odak noktası için bir ölçüt belirlemeye çalışmaktadır. Bu çalışmada, imge kalite değerlendirilmesinde kullanılan dayanaksız (referanssız) bir bulanıklık ölçütü bir takım uyarlamalardan geçirilmekte ve uyarlanan bu ölçüt KÖ kamera otomatik odaklanması problemi için önerilmektedir. Gerçekştirilen deneysel çalışmalar önerilen yöntemin KÖ kamera otomatik odaklanması probleminde başarıyla kullanılabileceğini göstermiştir.Item Open Access Image quality assessment using two-dimensional complex mel-cepstrum(SPIE, 2016) Cakir, S.; Çetin, A. EnisAssessment of visual quality plays a crucial role in modeling, implementation, and optimization of image-and video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extraction in the IQA framework, various algorithms have been used and recently, the two-dimensional (2-D) mel-cepstrum (2-DMC) feature extraction scheme has provided promising results in a feature-based IQA framework. However, the 2-DMC feature extraction scheme completely loses image-phase information that may contain high-frequency characteristics and important structural components of the image. In this work, "2-D complex mel-cepstrum" is proposed for feature extraction in an IQA framework. The method tries to integrate Fourier transform phase information into the 2-DMC, which was shown to be an efficient feature extraction scheme for assessment of image quality. Support vector regression is used for feature pooling that provides mapping between the proposed features and the subjective scores. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the image-phase information.Item Open Access Scalable image quality assessment with 2D mel-cepstrum and machine learning approach(Elsevier, 2011-07-19) Narwaria, M.; Lin, W.; Çetin, A. EnisMeasurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.