There is an urgent need to extract key information automatically from video for the purposes of indexing, fast retrieval and scene analysis. To support this vision, reliable scene change detection algorithms must be developed. This paper describes a novel unified algorithm for scene change detection in uncompressed and MPEG-2 compressed video sequences using statistical features of images. Results on video of various content types are reported and validated with the proposed scheme in uncompressed and MPEG-2 compressed video. Furthermore, results show that the accuracy of the detected transitions is above 95% and 90% for uncompressed and MPEG-2 compressed video respectively.
Abstract Aim To determine the accuracy with which Royal College of Surgeons of England (RCSEng) standards for documenting surgical procedures were met within a defined patient group, with the goal of improving patient safety, and improving documentation for medicolegal purposes. Method We conducted a closed loop audit on adherence to RCSEng standards for operation notes. We calculated the percentage adherence for each measurable standard and overall note, and presented the data at a local clinical governance meeting. We implemented autotext templates for operations to improve quality of operation notes in following RCSEng standards. We collected data retrospectively after 3 months to complete the audit cycle. Results We included 24 patients in the first cycle and 16 patients in the second cycle. Prior to intervention, the overall percentage of measurable standards met was 69.4%, which increased to 76.4% after intervention. The five least adherent standards all improved in percentage, with the largest increase from 33.3% to 93.8%. Conclusions In the first cycle, 3 of the measurable standards were met 100% of the time, which increased to 4 in the second cycle. Along with the general improvement of standards met, the interventions improved patient safety and surgeon documentation. Operation notes using templates generally recorded higher percentages of standards met than those without templates, but further work is needed to ensure more standards meet the 100% ideal.
A new scheme for MIMO video transmission using multiple-description coding is proposed. Significant improvements are demonstrated over conventional video transport methods. These are further improved through intelligent transmit power allocation, which yields improvements in average PSNR of up to 11 dB compared to standard, single-description video transmission.
Simulated HIPERLAN/2 physical layer results are contrasted using wideband measurements for omnidirectional and sectorised antennas. Gains as high as 13.4 dB are observed using a 60° sectorised antenna. Results demonstrate sectorised antenna improvements for all environments, even those where spatial filtering reduces the degree of frequency selective fading.
Multiple description (MD) video coding generates several descriptions so that any subset of descriptions can reconstruct video, which provides much error resilience. But most of the current MD video coding schemes are for two descriptions and for "on-off" channels, which is not suitable for packet-loss networks. This paper proposes a scheme to enhance the error resilience of traditional MD video coding in such environments, by periodically inserting S frames, a kind of switching frame, in the video stream to make the good description recover the 'bad' description, with very small redundancy. This proves to perform well in packet lossy networks especially at lower packet loss rate.
A number of lifting-based video coding schemes have been recently proposed for scalable video coding. We present a novel multi-view image codec based on a wavelet lifting scheme. The proposed lifting scheme with disparity compensated channel filtering is very efficient in terms of compression performance, memory requirements and implementation. We propose a number of enhancements to the basic scheme, such as hybrid prediction, adaptive weighing in update step and overlapped block disparity compensation which yield significant improvements in rate distortion performance. Experimental results show image quality gains of up to 1.5 dB and 1.2 dB against well established methods such as block-matching Haar and 5/3 wavelet lifting respectively.
Image fusion is the process of combining images of differing modalities, such as visible and infrared (IR) images. Significant work has recently been carried out comparing methods of fused image assessment, with findings strongly suggesting that a task-centred approach would be beneficial to the assessment process. The current paper reports a pilot study analysing eye movements of participants involved in four tasks. The first and second tasks involved tracking a human figure wearing camouflage clothing walking through thick undergrowth at light and dark luminance levels, whilst the third and fourth task required tracking an individual in a crowd, again at two luminance levels. Participants were shown the original visible and IR images individually, pixel-averaged, contrast pyramid, and dual-tree complex wavelet fused video sequences. They viewed each display and sequence three times to compare inter-subject scanpath variability. This paper describes the initial analysis of the eye-tracking data gathered from the pilot study. These were also compared with computational metric assessment of the image sequences
This paper describes a texture model application for future video compression algorithms. This employs region-based texture analysis techniques and advanced motion models to synthesise and warp video frames rather than encode the whole image or the prediction residual after traditional motion compensation. The proposed texture warping method and the dynamic texture model are integrated into an H.264 framework together with video quality assessment modules to prevent video artefacts. The results show impressive bitrate savings, up to 47%, over H.264 with similar visual quality.
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