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.
Texture granularity is an important visual characteristic that is useful in a variety of applications, including analysis, recognition, and compression, to name a few. A texture granularity measure can be used to quantify the perceived level of texture granularity. The granularity level of the textures is influenced by the size of the texture primitives. A primitive is defined as the smallest recognizable repetitive object in the texture. If the texture has large primitives then the perceived granularity level tends to be lower as compared to a texture with smaller primitives. In this work we are presenting a texture granularity database referred as GranTEX which consists of 30 textures with varying levels of primitive sizes and granularity levels. The GranTEX database consists of both natural and man-made textures. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective metric that automatically measures the perceived granularity level of textures is also presented as part of this work. It is shown that the proposed granularity metric correlates well with the subjective granularity scores.
Blur is an important attribute in the study and modeling of the human visual system. Blur discrimination was studied
extensively using 2D test patterns. In this study, we present the details of subjective tests performed to measure blur
discrimination thresholds using stereoscopic 3D test patterns. Specifically, the effect of disparity on the blur
discrimination thresholds is studied on a passive stereoscopic 3D display. The blur discrimination thresholds are
measured using stereoscopic 3D test patterns with positive, negative and zero disparity values, at multiple reference blur
levels. A disparity value of zero represents the 2D viewing case where both the eyes will observe the same image. The
subjective test results indicate that the blur discrimination thresholds remain constant as we vary the disparity value. This
further indicates that binocular disparity does not affect blur discrimination thresholds and the models developed for 2D
blur discrimination thresholds can be extended to stereoscopic 3D blur discrimination thresholds. We have presented
fitting of the Weber model to the 3D blur discrimination thresholds measured from the subjective experiments.
Three-D (3-D) stereo video is becoming widely available and one need to consider depth effects
when extending the 2-D video processing algorithms to 3-D stereo set-up. Depth is the additional
attribute which contributes to overall visual quality of the 3-D stereo video. Sharpness enhancement
algorithm is commonly applied in 2-D video processing chain; the effect of depth perception when
sharpness enhancement algorithm is applied to the 3-D stereo video is studied. A subjective
experiment is presented to study the relation between blur/sharpness and depth. A concept of just
noticeable blur (JNB) at different depths is introduced for the stereo image pairs. Based on the
results of the experiment an adaptive sharpness enhancement algorithm is proposed. The visual
quality results, of the proposed depth aware sharpness enhancement algorithm, are presented.
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