In this paper, we propose an algorithm that reduces unnecessary computations, while keeping almost same prediction quality as that of the full search algorithm. In the proposed algorithm, we can reduce unnecessary computations efficiently by calculating initial matching error point from first partial errors. To do that, we use tighter elimination condition as error criterion than the conventional PDE algorithm. Additionally, we use different search strategy compared with conventional spiral search pattern. By doing that, we can increase the probability that hits minimum error point as soon as possible. Our algorithm decreases the computational amount by about 50% of the conventional PDE algorithm without any degradation of prediction quality.
Rotation-invariant texture classification is one of the most challenging problems in computer vision. We present a new and effective method for rotation-invariant texture classification based on circular Gabor wavelets. Two group features can be constructed by the mean and variance of the circular Gabor filtered images and rotation invariants. Using the two group features, a discriminant can be found to classify rotated images. The proposed method is evaluated on three public texture databases: Brodatz, CUReT, and UIUCTex. The experimental results, based on different testing data sets, show that the proposed method has comparatively high correct classification rates not only for the rotated images, but also for the images under different illuminations and viewing directions. The proposed method is robust to additive white noise.
In this paper, we propose a new lossless MRME algorithm applicable to the current international video coding standards in which we remove only unnecessary computation in calculating block-matching error without any degradation of prediction quality. Our proposed algorithm employs the MRME scheme, the PDE (Partial Distortion Elimination), the spiral search, and the adaptive matching scan from the image complexity of the reference block. Important thing in the PDE algorithm is that how fast impossible candidates are detected by removing unnecessary computation. In this paper, we use the fact that the block-matching error is proportional to the complexity of the reference block with Taylor series expansion. The motivation of the proposing algorithm is using image complexity to find the impossible candidates faster. Local complexity of subblock is defined as spatial complexity of image data for each subblock and measured with gradient magnitude. From the experimental results, our proposed algorithm saves 50%~80% compared with the computations of the original MRME algorithm, while our proposed algorithm has the same prediction quality as that of the original MRME algorithm. Our proposed algorithm is applicable to the MPEG video codec such as MPEG-2 and MPEG-4 AVC and will be useful to real-time video coding applications.