Towards a Referenceless Visual Quality Assessment Model Using Binarized Statistical Image Features

In many practical multimedia applications, the visual content is modified during transmission, enhancement, modification, and compression stages. These modifications often create visible distortions that may be perceived by humans. Therefore, the development of algorithms that are able to assess the visual quality as perceived by a human viewer can lead to significant progress in multimedia applications. Many researchers have developed algorithms that estimate visual quality. These algorithms can either make use of the full pristine content (full-reference metrics), partial aspects of the pristine content (reduced-reference metrics) or only the assessed content (referenceless or no-reference metrics). These three approaches have advantages and drawbacks. Nevertheless, although the design of a referenceless metric is more challenging, they have greater applicability in different scenarios. This paper introduces a novel referenceless image quality assessment (RIQA) metric. The proposed metric uses statistics of the Binarized Statistical Image Features descriptor (BSIF) to analyze the textures of an image. These statistics are mapped into subjective quality scores using a Random Forest Regression approach. Results show that the proposed metric is robust and accurate, outperforming other state-of-the-art RIQA methods.