Blind image quality evaluation using perception based features

This paper proposes a novel no-reference Perception-based Image Quality Evaluator (PIQUE) for real-world imagery. A majority of the existing methods for blind image quality assessment rely on opinion-based supervised learning for quality score prediction. Unlike these methods, we propose an opinion unaware methodology that attempts to quantify distortion without the need for any training data. Our method relies on extracting local features for predicting quality. Additionally, to mimic human behavior, we estimate quality only from perceptually significant spatial regions. Further, the choice of our features enables us to generate a fine-grained block level distortion map. Our algorithm is competitive with the state-of-the-art based on evaluation over several popular datasets including LIVE IQA, TID & CSIQ. Finally, our algorithm has low computational complexity despite working at the block-level

AUTOMATED SYSTEM AND METHOD OF RETAINING IMAGES BASED ON A USER'S FEEDBACK ON IMAGE QUALITY

An automated system and method for retaining images in a smart phone are disclosed . The system may then determine a no - reference quality score of the image using a PIQUE module . The PIQUE module utilizes block level features of the image to determine the no - reference quality score . The system may present the image and the no - reference quality score to the user and accept a feedback towards quality of the image .The system may utilize a supervised learning model for continually learning a user ' s perception of quality of the image , the no -reference quality score determined by the PIQUE module , and the user feedback . Based on the learning , the supervised learning model may adapt the no - reference quality score and successively the image may either be retained or isolated for deletion , based on the adapted quality score and a predefined threshold rang

Blind distortion classification using content and perception based features

We propose a novel COntent & Perception based features for DIstortion Classification (COPDIC) that can be used for efficient prediction of different distortions that are present in real world imagery. Unlike existing statistical methods, our approach uses human perception to derive features from local block level characteristics to classify common distortion types in images. Given an image with distortions, this paper presents features and a classification methodology that can be used to accurately predict the distortion type (like JPEG, Blur, JP2K, White Noise). The reported classification accuracies compete well with the state-of-the-art techniques for LIVE IQA, TID & CSIQ databases. The proposed technique has low computational complexity and can be employed for real-time applications