Jorge E. Caviedes

No-reference sharpness metric based on local edge kurtosis

Sharpness metrics that use the frequency spectrum of the image cannot separate the sharpness information from scene content. Sharpness metrics that use spatial gradients of the edges of objects work only for comparisons among versions of the same image. We have developed a content independent, no-reference metric. In this approach, we create an edge profile by detecting edge pixels and enclosing them with 8/spl times/8 pixel blocks. For each block we compute sharpness using kurtosis of the DCT. The final metric is the average sharpness of the blocks in the edge profile. The advantages of this method include robust combination of spatial and frequency domain information, flexibility in the edge detection component, and a normalized sharpness scale. The results show high correlation with subjective quality for sharpness-enhanced video.

No reference PSNR estimation for compressed pictures

Many user-end applications require an estimate of the quality of coded video or images without having access to the original, i.e. a no-reference quality metric. Furthermore, in many such applications the compressed video bitstream is also not available. This paper describes methods for using the statistical properties of intra coded video data to estimate the quantization error caused by compression without accessing either the original pictures or the bitstream. We derive closed form expressions for the quantization error in coding schemes based on the discrete cosine transform and block based coding. A commonly used quality metric, the peak signal to noise ratio (PSNR) is subsequently computed from the estimated quantization error. Since quantization error is the most significant loss incurred during typical coding schemes, the estimated PSNR, or any PSNR-based quality metric may be used to gauge the overall quality of the pictures.

No-reference quality metric for degraded and enhanced video

In this paper we present a no-reference objective quality metric (NROQM) that has resulted from extensive research on impairment metrics, image feature metrics, and subjective image quality in several projects in Philips Research, and participation in the ITU Video Quality Experts Group. The NROQM is aimed at requirements including video algorithm development, embedded monitoring and control of image quality, and evaluation of different types of display systems. NROQM is built from metrics for desirable and non-desirable image features (sharpness, contrast, noise, clipping, ringing, and blocking artifacts), and accounts for their individual and combined contributions to perceived image quality. We describe our heuristic, incremental approach to modeling quality and training the NROQM, and its advantages to deal with imperfect data and imperfect metrics. The results of training the NROQM using a large set of video sequences, which include degraded and enhanced video, show high correlation between objective and subjective scores, and the results of the first performance test show good objective-subjective correlations as well. We also discuss issues that require further research such as fully content-independent metrics, measuring over-enhanced video quality, and the role of temporal impairment metrics.

Impairment metrics for digital video and their role in objective quality assessment

In this paper we discuss work on quantification of video impairments resulting from MPEG compression, their role, and their scope of application for objective quality assessment. Three important metrics, blocking artifacts level, ringing artifact level, and corner outlier artifact level have been used to create a combined impairment metric. The relevance of this metric to develop an objective quality assessment has been investigated, as well as the issues facing the creation of a no-reference quality metric. The main issues are overall metric completeness, and performance of the individual metric components. The impairment metrics that we have studied appear to be key components for future no- reference type of objective quality metrics. Impairment metrics are also of great importance because they allow closing the detect-measure-correct loop that is necessary to improve image quality in real time. Applications of single- ended quality metrics include multimedia home terminals, STBs, digital TV, and low bit-rate video applications such as IP videotelephony and video streaming over IP.