Monitoring of audio visual quality by key indicators

Fernández, Ignacio Blanco; Leszczuk, Mikołaj

doi:10.1007/s11042-017-4454-y

Cited by 3 publications

(1 citation statement)

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“…Indeed Video IQAs are rather far of classic IQAs [ 27 ], since they use spatio-temporal metrics, discussed in VQEG, and they are mainly devoted to compressed videos (MPEGs). Some recent works, more oriented to CGIs [ 28 ], and using a specific dataset (MOAVI) have been presented. Nevertheless these works are devoted to specific artifacts, and the MOAVI project seems to be difficult to apply for the detection of Monte Carlo noise at the moment, thus these techniques have not been used.…”

Section: Previous Workmentioning

confidence: 99%

Stopping Criterion during Rendering of Computer-Generated Images Based on SVD-Entropy

Buisine

Bigand

Synave

et al. 2021

Entropy

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The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculation process. Unlike natural-scene images, however, a reference image is not available for computer-generated images. Thus, classic methods to assess noise quantity and stopping criterion during the rendering process are not usable. This is particularly important in the case of global illumination methods based on stochastic techniques: They provide photo-realistic images which are, however, corrupted by stochastic noise. This noise can be reduced by increasing the number of paths, as proved by Monte Carlo theory, but the problem of finding the right number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. Until now, the features taking part in the human evaluation of image quality and the remaining perceived noise are not precisely known. Synthetic image generation tends to be very expensive and the produced datasets are high-dimensional datasets. In that case, finding a stopping criterion using a learning framework is a challenging task. In this paper, a new method for characterizing computational noise for computer generated images is presented. The noise is represented by the entropy of the singular value decomposition of each block composing an image. These Singular Value Decomposition (SVD)-entropy values are then used as input to a recurrent neural network architecture model in order to extract image noise and in predicting a visual convergence threshold of different parts of any image. Thus a new no-reference image quality assessment is proposed using the relation between SVD-Entropy and perceptual quality, based on a sequence of distorted images. Experiments show that the proposed method, compared with experimental psycho-visual scores, demonstrates a good consistency between these scores and stopping criterion measures that we obtain.

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Section: Previous Workmentioning

confidence: 99%