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A perceptual image coder for the compression of monochrome images is presented here, in which the coding structure is coupled with a vision model to produce coded images with an improved visual quality at low bit-rates. The coder is an improvement on the Joint Photographic Experts Group (JPEG -Discrete Cosine Transform (DCT) based) image compression(More)
In this paper, a new denoising technique for images corrupted with additive white Gaussian noise is presented. The technique used here is to combine the wavelet transform and the curvelet transform with anisotropic diffusion. Curvelet transform is a new geometric-based multiscale transform developed to give sparse representation of images with singularities(More)
High Efficiency Video Coding (HEVC) is the most recent video compression standard that achieves higher encoding efficiency over earlier popular standards like MPEG-2 and H.264/AVC. By adopting a variety of coding efficiency enhancement and parallel processing tools, HEVC is in a position to provide up to 50% more bit-rate reduction over its precursor(More)
Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is(More)
High efficiency video coding (HEVC), the most recent video compression standard, offers about double the compression ratio over its immediate predecessor H.264/AVC at the same level of video quality or substantially higher video quality at the same bit-rate. Careful refinement of existing tools, as well as the introduction of a variety of parallel(More)
A data driven background subtraction algorithm where each background pixel is modeled with a representative set of samples is presented. The samples are pixel values observed in preceding frames. Each pixel in an incoming frame is classified as background or foreground by comparing the pixel value with the samples in pixel's background model. The background(More)
Vehicle detection and classification is the most important and challenging stage of traffic surveillance using computer vision techniques. The videos captured using the closed circuit television (CCTV) cameras placed in roadsides or driveways are used for the surveillance. The surveillance system includes detection of moving vehicles, counting the number of(More)