Natural and artificial textures occur frequently in images and in video sequences. Image/video coding systems based on texture synthesis can make use of a reliable texture synthesis quality assessment method in order to improve the compression performance in terms of perceived quality and bit-rate. Existing objective visual quality assessment methods do not perform satisfactorily when predicting the synthesized texture quality. In our previous work, we showed that texture regularity can be used as an attribute for estimating the quality of synthesized textures. In this paper, we study the effect of another texture attribute, namely texture granularity, on the quality of synthesized textures. For this purpose, subjective studies are conducted to assess the quality of synthesized textures with different levels (low, medium, high) of perceived texture granularity using different types of texture synthesis methods.
KEYWORDS: Cones, Spatial frequencies, Visual system, Visualization, Quantization, Data modeling, Visual process modeling, Curium, High dynamic range imaging, Psychophysics
The human visual system’s luminance nonlinearity ranges continuously from square root behavior in the very dark, gamma-like behavior in dim ambient, cube-root in office lighting, and logarithmic for daylight ranges. Early display quantization nonlinearities have been developed based on luminance bipartite JND data. More advanced approaches considered spatial frequency behavior, and used the Barten light-adaptive Contrast Sensitivity Function (CSF) modelled across a range of light adaptation to determine the luminance nonlinearity (e.g., DICOM, referred to as a GSDF {grayscale display function}). A recent approach for a GSDF, also referred to as an electrical-to-optical transfer function (EOTF), using that light-adaptive CSF model improves on this by tracking the CSF for the most sensitive spatial frequency, which changes with adaptation level. We explored the cone photoreceptor’s contribution to the behavior of this maximum sensitivity of the CSF as a function of light adaptation, despite the CSF’s frequency variations and that the cone’s nonlinearity is a point-process. We found that parameters of a local cone model could fit the max sensitivity of the CSF model, across all frequencies, and are within the ranges of parameters commonly accepted for psychophysicallytuned cone models. Thus, a linking of the spatial frequency and luminance dimensions has been made for a key neural component. This provides a better theoretical foundation for the recently designed visual signal format using the aforementioned EOTF.
Transform coding using the discrete cosine transform is one of the most popular techniques for image and video compression. However, at low bit rates, the coded images suffer from severe visual distortions. An innovative approach is proposed that deals with artifacts in JPEG compressed images. Our algorithm addresses all three types of artifacts which are prevalent in JPEG images: blocking, and for edges blurring, and aliasing. We enhance the quality of the image via two stages. First, we remove blocking artifacts via boundary smoothing and guided filtering. Then, we reduce blurring and aliasing around the edges via a local edge-regeneration stage. We compared the proposed algorithm with other modern JPEG artifact-removal algorithms. The results demonstrate that the proposed approach is competitive, and can in many cases outperform, competing algorithms.
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