We present a heuristic solution for <i>B</i>-term approximation using
Tree-Structured Haar (TSH) transforms. Our solution consists of two
main stages: best basis selection and greedy approximation. In
addition, when approximating the same signal with different B
constraint or error metric, our solution also provides the
flexibility of having less overall running time at expense of more
storage space. We adopted lattice structure to index basis vectors,
so that one index value can fully specify a basis vector. Based on
the concept of fast computation of TSH transform by butterfly
network, we also developed an algorithm for directly deriving
butterfly parameters and incorporated it into our solution. Results
show that, when the error metric is normalized ℓ<sub>1</sub>-norm and
normalized ℓ<sub>2</sub>-norm, our solution has comparable (sometimes
better) approximation quality with prior data synopsis algorithms.
In this study we investigate the visibility and annoyance of simulated defective sub-pixels in a liquid crystal display (LCD). The stimulus was a rectangular image containing one centered object with a gray surround and a single defective pixel. The surround was either uniform gray or a gray-level texture. The target was a simulated discolored pixel with one defective sub-pixel (green, red or blue) and two normally functioning sub-pixels. On each trial, it was presented at a random position. Subjects were asked to indicate if they saw a defective pixel, and if so, where it was located and how annoying it was. For uniform surrounds, our results show that detection probability falls slowly for green, faster for red, and fastest for blue as background luminance increases. When detection probability is plotted against luminance contrast green defective pixels are still most detectable, then red, then blue. Mean annoyance value falls faster than detection probability as background luminance increases, but the trends are the same. A textured surround greatly reduces the detection probability of all defective pixels. Still, green and red are more detectable than blue. With the textured surround the mean annoyance tends to remain high even when detection probability is quite low. For both types of surrounds, probability of detection is least for targets in the bottom region of the image.