Wavelet entropy based no-reference quality prediction of distorted/decompressed images

Assessing quality of distorted/decompressed images without reference to the original image is difficult due to vagueness in extracted features and complex relation between features and visual quality of images. The paper aims at assessing the quality of distorted/decompressed images without any reference to the original image by developing a fuzzy inference system (FIS). First level Haar approximation entropies of test images of LIVE database and the features extracted using the five Benchmark images are considered as antecedents while mean opinion score (MOS) based quality of the images are used as consequent to the proposed FIS. The crisp value of the features and quality of the images are expressed using linguistic variables, which are fuzzified to measure the vagueness in extracted features. Takagi-Sugeno-Kang (TSK) inference rule has been applied to the FIS to predict the quality of a new distorted/decompressed image. Quality of decompressed and various noise incorporated test images are predicted without reference to the original image producing output comparable with other no reference techniques. Results are validated with the objective and subjective image quality measures. Prediction characteristics are also evolved to verify the application of the proposed system in quality prediction.