The non-reference method is widely useful to estimation picture quality on the decoder side. In this paper, we discuss the estimation method for spatial blur that divides the frequency zones by the absolute value of 64 coefficients with an 8-by-8 DCT and compares them. It is recognized that absolute blur estimation is possible with the decoded picture only.

A wide variety of visual textures could be successfully modeled as spatially variant by quantitatively describing them through the variation of their local spatial frequency and/or local orientation components. This class of patterns includes flow-like, granular or oriented textures. Modeling is achieved by assuming that locally, textured images contain a single dominant component describing their local spatial frequency and modulating amplitude or contrast. Spatially variant textures are non-homogeneous in the sense of having nonstationary local spectra, while remaining locally coherent. Segmenting spatially variant textures is the challenging task undertaken in this paper. Usually, the goal of texture segmentation is to split an image into regions with homogeneous textural properties. However, in the case of image regions with spatially variant textures, there is no global homogeneity present and thus segmentation passes through identification of regions with globally nonstationary, but locally coherent, textural content. Local spatial frequency components are accurately estimated using Gabor wavelet outputs along with the absolute magnitude of the convolution of the input image with the first derivatives of the underlying Gabor function. In this paper, a frequency estimation approach is used for segmentation. Indeed, at the boundary between adjacent textures, discontinuities occur in texture local spatial frequency components. These discontinuities are interpreted as corresponding to texture boundaries. Experimental results are in remarkable agreement with human visual perception, and demonstrate the effectiveness of the proposed technique.

A novel neural network architecture for image texture classification is introduced. The proposed model (Kernel Modifying Neural Network: KM Net) which incorporates the convolution filter kernel and the classifier in one, enables an automated texture feature extraction in multichannel texture classification through the modification of the kernel and the connection weights by the backpropagation-based training rule. The first layer units working as the convolution kernels are constrained to be an array of Gabor filters, which achieves a most efficient texture feature localization. The following layers work as a classifier of the extracted texture feature vectors. The capability of the KM Net and its training rule is verified using a basic problem on a synthetic texture image. In addition, the possibilities of applying the KM Net to natural texture classification and biological tissue classification using an ultrasonic echo image have been tried.