Cube mapping is widely used in many graphics applications due to the availability of hardware support. However, it does not sample the spherical surface evenly. Recently, a uniform spherical mapping, isocube mapping, was proposed. It exploits the six-face structure used in cube mapping and samples the spherical surface evenly. Unfortunately, some texels in isocube mapping are not rectilinear. This nonrectilinear property may degrade the filtering quality. This paper proposes a novel spherical mapping, namely unicube mapping. It has the advantages of cube mapping (exploitation of hardware and rectilinear structure) and isocube mapping (evenly sampling pattern). In the implementation, unicube mapping uses a simple function to modify the lookup vector before the conventional cube map lookup process. Hence, unicube mapping fully exploits the cube map hardware for real-time filtering and lookup. More importantly, its rectilinear partition structure allows a direct and real-time acquisition of the texture environment. This property facilitates dynamic environment mapping in a real time manner.

This paper proposes a novel multiscale spherical radial basis function (MSRBF) representation for all-frequency lighting. It supports the illumination of distant environment as well as the local illumination commonly used in practical applications, such as games. The key is to define a multiscale and hierarchical structure of spherical radial basis functions (SRBFs) with basis functions uniformly distributed over the sphere. The basis functions are divided into multiple levels according to their coverage (widths). Within the same level, SRBFs have the same width. Larger width SRBFs are responsible for lower frequency lighting while the smaller width ones are responsible for the higher frequency lighting. Hence, our approach can achieve the true all-frequency lighting that is not achievable by the single-scale SRBF approach. Besides, the MSRBF approach is scalable as coarser rendering quality can be achieved without reestimating the coefficients from the raw data. With the homogeneous form of basis functions, the rendering is highly efficient. The practicability of the proposed method is demonstrated with real-time rendering and effective compression for tractable storage.

An illumination adjustable image (IAI) contains a large number of prerecorded images under various light directions. Relighting a scene under complicated lighting conditions can be achieved from the IAI. Using the radial basis function (RBF) approach to represent an IAI is proven to be more efficient than using the spherical harmonic approach. However, to represent high-frequency lighting effects, we need to use many RBFs. Hence, the relighting speed could be very slow. This brief investigates a partial reconstruction scheme for relighting an IAI based on the locality of RBFs. Compared with the conventional RBF and spherical harmonics (SH) approaches, the proposed scheme has a much faster relighting speed under the similar distortion performance.

In image-based relighting, a pixel is associated with a number of sampled radiance values. This paper presents a two-level compression method. In the first level, the plenoptic property of a pixel is approximated by a spherical radial basis function (SRBF) network. That means that the spherical plenoptic function of each pixel is represented by a number of SRBF weights. In the second level, we apply a wavelet-based method to compress these SRBF weights. To reduce the visual artifact due to quantization noise, we develop a constrained method for estimating the SRBF weights. Our proposed approach is superior to JPEG, JPEG2000, and MPEG. Compared with the spherical harmonics approach, our approach has a lower complexity, while the visual quality is comparable. The real-time rendering method for our SRBF representation is also discussed.

Spherical harmonic (SH) basis functions have been widely used for representing spherical functions in modeling various illumination properties. They can compactly represent low-frequency spherical functions. However, when the unconstrained least square method is used for estimating the SH coefficients of a hemispherical function, the magnitude of these SH coefficients could be very large. Hence, the rendering result is very sensitive to quantization noise (introduced by modern texture compression like S3TC, IEEE half float data type on GPU, or other lossy compression methods) in these SH coefficients. Our experiments show that, as the precision of SH coefficients is reduced, the rendered images may exhibit annoying visual artifacts. To reduce the noise sensitivity of the SH coefficients, this paper first discusses how the magnitude of SH coefficients affects the rendering result when there is quantization noise. Then, two fast fitting methods for estimating the noise-resistant SH coefficients are proposed. They can effectively control the magnitude of the estimated SH coefficients and, hence, suppress the rendering artifacts. Both statistical and visual results confirm our theory.

In the area of image-based modeling and rendering (IBMR), a new scene under a novel illumination condition is synthesized by interpolating reference images. However, for good quality rendering tremendous reference images under various illumination conditions are required. We present a two-level compression method for reference images. In the first level, the spherical harmonic transform is used to approximate the plenoptic function of each pixel In the second level, we use the embedded zerotrees wavelet (EZW) method for removing the spatial redundancy in spherical coefficients produced from the first level process. Simulation results show our approach is much superior than compressing reference images with JPEG.

The global extended Kalman filtering (EKF) algorithm for recurrent neural networks (RNNs) is plagued by the drawback of high computational cost and storage requirement. In this paper, we present a local EKF training-pruning approach that can solve this problem. In particular, the by-products, obtained along with the local EKF training, can be utilized to measure the importance of the network weights. Comparing with the original global approach, the proposed local approach results in much lower computational cost and storage requirement. Hence, it is more practical in solving real world problems. Simulation showed that our approach is an effective joint-training-pruning method for RNNs under online operation.