Neighbor cache prefetching for multimedia image and video processing

Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching

Geometric distortion measurement for shape coding: a contemporary review

Geometric distortion measurement and the associated metrics involved are integral to the rate-distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this paper presenting a comprehensive review of geometric distortion measurement, the diverse metrics applied and their impact on shape coding. The respective performance of these measuring strategies is analysed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterisation being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation

NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO

The goal of this thesis is to find a highly efficient algorithm to compress patient monitoring video. This type of video mainly contains local motions and a large percentage of idle periods. To specifically utilize these features, we present an object-based approach, which decomposes input video into three objects representing background, slow-motion foreground and fast-motion foreground. Encoding these three video objects with different temporal scalabilities significantly improves the coding efficiency in terms of bitrate vs. visual quality. The video decomposition is built upon change detection which identifies content changes between video frames. To improve the robustness of capturing small changes, we contribute two new change detection models. The model built upon Markov random theory discriminates foreground containing the patient being monitored. The other model, called covariance test method, identifies constantly changing content by exploiting temporal correlation in multiple video frames. Both models show great effectiveness in constructing the defined video objects. We present detailed algorithms of video object construction, as well as experimental results on the object-based coding of patient monitoring video

Fourier Transform to Detect Pine Seedlings in a Digital Image

Each year, u.s. forest nurseries produce approximately 200 million pine seedlings. Forest companies depend on an adequate number of seedlings in order to replant timber land. To monitor the progress of seedlings, nurseries periodically conduct an inventory. The procedure is performed manually and is based on a statistical estimate. The process is slow, tedious, and imprecise. Automating the inventory procedure is subject of this dissertation. A digital image processing technique to visually count pine seedlings is investigated. The technique is based on a proposed imaging system which resides on a platform behind a tractor. As the system passes over the seedling bed, image sensors capture an overhead view of individual seedlings. A computer analyzes the sensor values in order to detect and count individual seedlings. This dissertation is concerned with developing a computer algorithm. Several test images were obtained. Pertinent seedling features in the images are gray level contrast, lines formed by the needles, and circular distribution of the needles. Four different techniques were investigated in an attempt to use these features to detect pine seedlings. These techniques are gray level peaks geometric intersection of needle lines, gray level contour encoding 1 and a technique based on the Fourier transform.Agricultural Engineerin