Details

Autor(en) / Beteiligte

• Narwaria, Manish
• Lin, Weisi
• Cetin, A. Enis

Titel

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Ist Teil von

• Pattern recognition, 2012, Vol.45 (1), p.299-313

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2012

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Measurement 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. ► To explore 2D mel-cepstrum features and their properties for perceptual image quality assessment. ► To use of machine learning for the feature pooling stage in image quality assessment. ► To formulate a new image quality metric as a supervised pattern recognition problem.