Uniform frequency images: adding geometry to images to produce space-efficient textures

Abstract

Abstract:We discuss the concept of uniform frequency images, which exhibit uniform local frequency properties. Such images make optimal use of space when sampled close to their Nyquist limit. A warping function may be applied to an arbitrary image to redistribute its local frequency content, reducing its highest frequencies and increasing its lowest frequencies in order to approach this uniform frequency ideal. The warped image may then be downsampled according to its new, reduced Nyquist limit, thereby reducing its storage requirements. To reconstruct the original image, the inverse warp is applied.We present a general, top-down algorithm to automatically generate a piecewise-linear warping function with this frequency balancing property for a given input image. The image size is reduced by applying the warp and then downsampling. We store this warped, downsampled image plus a small number of polygons with texture coordinates to describe the inverse warp. The original image is later reconstructed by rendering the associated polygons with the warped image applied as a texture map, a process which is easily accelerated by current graphics hardware. As compared to previous image compression techniques, we generate a similar graceful space-quality tradeoff with the advantage of being able to "uncompress" images during rendering. We report results for several images with sizes ranging from 15,000 to 300,000 pixels, achieving reduction rates of 70-90% with improved quality over downsampling alone.

Visual saliency guided textured model simplification

“…Besides the global methods as described above, texture map simplification can also be performed to local regions. [13] recursively resized texture map regions by balancing the amount of frequency content in each region. [1] determined the frequency signal in local texture map regions, stretching or shrinking a region based on its importance indicated by the frequency signal.…”

Optimization Techniques for 3D Graphics Deployment on Mobile Devices

“…As earlier mentioned, the capabilities of mobile hardware for 3D graphics re-production grow On-device optimization [8] x [33] x x [34] x [35] x [36] x [37] x [38] x x [39] x [40] x x [41] x [42] x [43] x x [44] x [53] x [54] x x [55] x x [56] x x [57] x x [58] x [60] x [61] x [62] x x [63] x x [64] x x [65] x [66] x [67] x [68] x [69] x [70] x [71] x [72] x [73] x [74] x x [76] x x [77] x [78] x [79] x [80] x [81] x [82] x [83] x [84] x extremely fast. For instance, 3D rendering performance of Tegra4 is roughly three-four times higher when compared to the previous generation [45].…”

“…There are also other methods which are similar in principle, but use different algorithms to perform the texture coordinate optimization. The method introduced by Hunter and Cohen [70] is based on the modification of the image frequency space. Their approach removes high and low frequency detail to harmonize the overall image frequency range and hence allow compression (scaling down) of the original image data.…”

Flattening Supervoxel Hierarchies by the Uniform Entropy Slice

“…The intuitive idea behind the uniform entropy slice is that we want to select nodes in the tree that balance the information contribution to the overall slice. We are inspired by the Uniform Frequency Images work of Hunter and Cohen [17]. Their model is proposed for image compression; they automatically generate an invertible warping function that downshifts the image's highest spatial frequencies in exchange for upshifting some of its lowest spatial frequencies, producing a concentration of mid-range frequencies.…”