  Conventional images/videos are often rendered within the central vision area
of the human visual system (HVS) with uniform quality. Recent virtual reality
(VR) device with head mounted display (HMD) extends the field of view (FoV)
significantly to include both central and peripheral vision areas. It exhibits
the unequal image quality sensation among these areas because of the
non-uniform distribution of photoreceptors on our retina. We propose to study
the sensation impact on the image subjective quality with respect to the
eccentric angle $\theta$ across different vision areas. Often times, image
quality is controlled by the quantization stepsize $q$ and spatial resolution
$s$, separately and jointly. Therefore, the sensation impact can be understood
by exploring the $q$ and/or $s$ in terms of the $\theta$, resulting in
self-adaptive analytical models that have shown quite impressive accuracy
through independent cross validations. These models can further be applied to
give different quality weights at different regions, so as to significantly
reduce the transmission data size but without subjective quality loss. As
demonstrated in a gigapixel imaging system, we have shown that the image
rendering can be speed up about 10$\times$ with the model guided unequal
quality scales, in comparison to the the legacy scheme with uniform quality
scales everywhere.
