Compression Of Patients Video

ABSTRACT

The compression video reduces the amount of data required to represent the video by removing redundant information. In Today’s life compression is very necessary, because dependency on computer increasing day by day. Fortunately, there are several methods of video compression available today, though the Video looks like continuous motion. It is actually a series of still images. The Set Portioning in Hierarchical Trees (SPHT) algorithm has been used for the compression of video, which is not suitable for some hospital applications. Due to selection of ROI the time consumption is more even for low quality videos. To overcome above disadvantages DWT has been implemented for patient video compression compression scheme that relies on transmitting high-quality video of moving. To increases the video quality of the transition between the regions of various qualities the smoothing edge concepts to be propose. The results to be analyse using DWT method with various targets and the visual assessment of a patient to be achieve and the result is obtained by implementing design on MATLAB Software.




Existing system

The existing method segments the ROI manually defined by the user by identifying its DCT coefficients; it uses a Set Portioning in Hierarchical Trees (SPIHT) compression known for its power consumption while maintaining average quality performance. The movements in the images to define the ROI, considering they represent arteries in angiograms images. It is based on removing the low-level contour let coefficients for the region that is considered diagnostically insignificantly.
Disadvantages:
This method was not suited for all the applications as it fails a high compression ratio. 
Quality of video is also not good

Proposed work:

To overcome the disadvantages from the existing method the discrete wavelet transform algorithm has been implemented. The subsequent step consists in reordering the stream and zero encoding it; that is grouping together the nonzero coefficients, in specific scan order, to compact the data and efficiently encode the zeros and it encodes the video with the ROI of higher quality than the background. The encoding video can evaluated the results of the visual quality. The proposed method estimates the quality from the overall quantization returned by the encoder.

Advantages:

It results in a high compression ratio.
Quality of video is good compared to the existing method
Extremely efficient for low motion video content.



SYSTEM REQUIREMENTS:

GENERAL:

The system requirement of the project is described and the specification of the software and hardware requirements of the project is described.

HARDWARE REQUIREMENTS:

Processor Type : Pentium -IV
Speed   : 2.4 GHZ
Ram : 128 MB RAM
Hard disk                                 : 20 GB HD


SOFTWARE REQUIREMENTS

Operating System             : Windows 7 
Software Programming Package : Matlab R2018a


LITERATURE SURVEY:

1. Title: Region-of-Interest Compression and View Synthesis for Light Field Video Streaming

Author: Wei Xiang et al.
Year:2019
Description: Light field videos provide a rich representation of real-world, thus the research of this technology is of urgency and interest for both the scientific community and industries. Light field applications such as virtual reality and post-production in the movie industry require a large number of viewpoints of the captured scene to achieve an immersive experience, and this creates a significant burden on light field compression and streaming. The existing method first present a light field video dataset captured with a preoptic camera. Then a new region-of-interest (ROI)-based video compression method is designed for light field videos. In order to further improve the compression performance, a novel view synthesis algorithm is presented to generate arbitrary viewpoints at the receiver. The experimental evaluation of four light field video sequences demonstrates that the ROI-based compression method can save 5%-7% in bitrates in comparison to conventional light field video compression methods. Furthermore, the existing view synthesis-based compression method not only can achieve a reduction of about 50% in bitrates against conventional compression methods, but the synthesized views can exhibit identical visual quality as their ground truth.


2.Title: Physiological Signal Preserving Video Compression for Remote Photoplethysmography

Author: Xingming Wu

Year:2019
Description : The consumer-level digital camera has become a physiological signal monitoring sensor due to the rapid growth of the remote photoplethysmography (rPPG) technique. However, rPPG suffers from the artifacts caused by video compression, a technique that widely exists in nearly all video-related applications. This limitation greatly narrows the application range of the rPPG. A novel physiological signal preserving video compression algorithm called POSSC is proposed such that the existing rPPG signal extraction approaches can be applied directly on the compressed video without modification. This process approach three main steps: 1) rPPG feature extraction; 2) sparse subspace clustering; and 3) region of interest (ROI)-based video compression. The existing models of the skin/non-skin feature classification problem as sparse subspace clustering. Physiological signals are preserved by allocating more (fewer) bits to the ROI (non-ROI) regions. A self-collected benchmark dataset is established to evaluate the performance of POSSC in terms of body part, ROI size, light source, illumination intensity, and multiple subjects in an image. The results demonstrate that POSCC is effective in preserving physiological signals for facial videos under normal light intensity, insensitive to ROI size, shape, and the number of subjects. 


3.Title: Scalable Wavelet-Based Coding of Irregular Meshes with Interactive Region-of-Interest Support
 
Author: Peter Lambert

Year:2018

Description: The existing work was based on a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. This approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10 6 ~ 10 7 vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices.


4.Title : A Novel Framework for Remote Photoplethysmography Pulse Extraction on Compressed Videos

Author : Changchen Zhao ; Chun-Liang Lin ; Weihai Chen ; Zhengguo Li

Year:2018

Description:

Remote photoplethysmography (rPPG) has recently attracted much attention due to its non-contact measurement convenience and great potential in health care and computer vision applications. However, almost all the existing rPPG methods are based on uncompressed video data, which greatly limits its application to the scenarios that require long-distance video transmission. This novel framework is a first attempt to address the rPPG pulse extraction in presence of video compression artifacts. Based on the analysis of the impact of various compression methods on rPPG measurements, the problem is cast as single-channel signal separation. The framework consists of three major steps to extract the pulse waveform and heart rate by exploiting frequency structure of the rPPG signal. A benchmark dataset which contains tationery and motion videos has been built. The results show that the algorithm significantly improves the SNR and heart rate precision of state-of-the-art rPPG algorithms on stationary videos and has a positive effect on motion videos at low bitrates.


5.Title : Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

Author: Miguel Hernández-Cabronero 

Year:2018

Description: 

The increasing availability of digital pathology images has motivated the design of tools to foster multidisciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilitates the transmission and access to pathology images by multiple users. However, the huge file size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. This technique based on novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-λ ) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the High-Efficiency Video Coding standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms the other state-of-the-art methods designed for single images by very accurately attaining the target bit rate of the non-RoI.

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