Data compression algorithm for computer vision applications: A survey

Computers with their growing demands have their applicability in every field, one of the applications is the computer vision, where the function of an human eye has been replaced by using various sensors to capture the environment in the similar way a human eye does. We know that capturing the environment through sensors lead to origin of various kinds of images and videos of different shapes and sizes and contain large amount of descriptive information. These features enable us to construct various models by using the geometry, physics, statistics. For completing our actions we make use of camera, cables, connecting devices and computer set. Similarly, the descriptive information needs to be transmitted over a channel to share with others and also for utilizing the storage resources efficiently we make use of data compression algorithms and compare which algorithm is best suited for a given application and measure the level of compression using entropy and compression ratios.

[1]  Mohammed M. Siddeq,et al.  Novel 3D Compression Methods for Geometry, Connectivity and Texture , 2016 .

[2]  Maria Trocan,et al.  An image compression for embedded eye-tracking applications , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).

[3]  Uwe Baumgarten,et al.  Influence of Image/Video Compression on Night Vision Based Pedestrian Detection in an Automotive Application , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[4]  Joel H. Saltz,et al.  ZPEG: A hybrid DPCM-DCT based approach for compression of Z-stack images , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Chakravartula Raghavachari,et al.  Efficient use of bandwidth by image compression for vision-based robotic navigation and control , 2014, 2014 International Conference on Communication and Signal Processing.

[6]  Wojciech Szpankowski,et al.  Compression of Graphical Structures: Fundamental Limits, Algorithms, and Experiments , 2012, IEEE Transactions on Information Theory.

[7]  S. Kantawong,et al.  Information signs compression and classification using vector quantization and neural network for blind man tourisms navigation system , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[8]  Imran Ullah Khan,et al.  Performance analysis of H.264 video decoder: Algorithm and applications , 2015, 2015 International Conference on Energy Economics and Environment (ICEEE).

[9]  Michael Bosse,et al.  Keep it brief: Scalable creation of compressed localization maps , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Mihaela van der Schaar,et al.  Near-lossless complexity-scalable embedded compression algorithm for cost reduction in DTV receivers , 2000, IEEE Trans. Consumer Electron..

[11]  Touradj Ebrahimi,et al.  Image transmorphing with JPEG , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[12]  Sukumar Brahma,et al.  Efficient Compression of PMU Data in WAMS , 2016, IEEE Transactions on Smart Grid.

[13]  Terry A. Welch,et al.  A Technique for High-Performance Data Compression , 1984, Computer.

[14]  Patrick Garda,et al.  High frame rate medical quality video compression for tele-EEG , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Peng Gao,et al.  Design and optimization of a parallel guidance device for minimal invasive spinal surgery , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[16]  Hongliang Zhu,et al.  Sea route monitoring system using wireless sensor network based on the data compression algorithm , 2014, China Communications.

[17]  Cheng-You Wang,et al.  Image compression using wavelet transform with lifting scheme and SPIHT in digital cameras for Bayer CFA , 2013, 2013 International Conference on Wavelet Analysis and Pattern Recognition.

[18]  KokSheik Wong,et al.  Moving object detection in HEVC video by frame sub-sampling , 2015, 2015 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[19]  Achuthsankar S. Nair,et al.  NGS read data compression using parallel computing algorithm , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[20]  Chandrashekhar Kamargaonkar,et al.  Region based medical image compression using block-based PCA , 2016, 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC).

[21]  P. D. Lawrence,et al.  Programmed data logging in forest harvesting using floating-aperture data compression , 1982, IEEE Transactions on Instrumentation and Measurement.

[22]  Chandan Kumar Jha,et al.  A novel ECG data compression algorithm using best mother wavelet selection , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[23]  S. Kantawong,et al.  High building exterior mirror cleaning robot based on image detection and compression analysis with fuzzy ladder control system , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[24]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[25]  V. Novotny,et al.  From Standard Definition to High Definition Migration in Current Digital Video Broadcasting , 2007, 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07).

[26]  Joydeep Ghosh,et al.  Face Detection on Distorted Images Augmented by Perceptual Quality-Aware Features , 2014, IEEE Transactions on Information Forensics and Security.

[27]  Guo-Zua Wu,et al.  High-Performance Sub-Picture Compression Algorithm Used in High-Definition Video Discs , 2007, IEEE Transactions on Magnetics.

[28]  Acacio Zimbico,et al.  Comparative study of the performance of the JPEG algorithm using optimized quantization matrices for ultrasound image compression , 2014, 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC).