3D point cloud data consist of a large number of points with attribute information such as color in addition to geometry information of 3D positions. Since their data size tends to be large, efficient compression methods not only for the geometry but also for the attribute information are desired. In general, attribute information has a spatial correlation depending on local texture of the object surface. However, the conventional adaptive prediction technique, which is popular in 2D image coding, cannot be applied as-is, since distribution of the already encoded samples used for the prediction is usually sparse and irregular. In this paper, we propose a method for designing adaptive predictors on a point-by-point basis using a 3D directional autocorrelation model of the attribute information. The obtained predictors are utilized in the probability model optimization technique for efficient lossless coding of RGB color attribution.
This paper proposes a method of detecting entry and exit of vehicles in each parking slot using a surveillance camera placed outdoors. To specify the respective parking slot areas in a surveillance camera image, polygonal windows are set by projecting rectangular boxes, each of which surrounds the typical body of a vehicle parked in each slot, onto the image based on the perspective projection matrix associated with the surveillance camera. The projection matrix is semi-automatically calculated by providing 3D coordinates to a small number of points picked up from the surveillance camera image. Then, a joint intensity histogram between a pair of images taken by the same camera with a certain time interval is calculated in each window. By analyzing the distribution of the histogram, entry and exit of vehicles in the slot can be robustly detected without being affected by lighting change during the time interval.
We previously proposed a method of designing a 2D FIR filter that can maximize the well-known objective quality index called SSIM. The designed filter can be used as a post processing tool for lossy image coding methods to reduce coding artifacts. In this scenario, there is a trade-off between the amount of side information on filter coefficients and the obtained gain in image quality. In this paper, effectiveness of the designed filters on the rate-SSIM based coding performance is evaluated under different settings of the size and quantization precision of the filter coefficients. Moreover, we introduce symmetric constraints on the filter coefficients to reduce the side information.
We previously proposed a novel lossless image coding method that utilizes example search and adaptive prediction within a framework of probability model optimization. In this paper, the definition of the probability model as well as its optimization procedure are modified to reduce the encoding complexity. In addition, affine predictors used in the adaptive prediction are refined for accurate probability modeling. Simulation results indicate that our modification contributes not only to encoding time reduction, but also to coding efficiency improvement for all of the tested images.
This paper describes a method of designing a 2D post filter for reducing coding artifacts caused by lossy image compression. Though Mean Squared Error (MSE) has been typically used in such filter design, it is not necessarily a good quality measure in terms of consistency with subjective perception. In this paper, we employ a more reliable quality measure called Structural SIMilarity (SSIM), and derive filter coefficients that can maximize the SSIM score for each image.
Recently, convolutional neural network-based generative models of image signals have been proposed mainly for the purpose of image generation, restoration and compression. For example, PixelCNN++ approximates probability distribution of the image intensity value as a parametric function pel-by-pel, and can be used for lossless image coding tasks. However, such an approach cannot work well for specific images which have statistical properties different from the image dataset used for the network training. In this paper, we improve the coding efficiency by introducing a few parameters for adjusting the probability model generated by PixelCNN++. These parameters are numerically optimized to minimize coding rates of the given image and then encoded as side-information to enable same adjustment at the decoder side.
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