PDF-Adaptive Image Compression Using

Saliency and KAZE Features Authors Siddharth Srivastava Prerana Mukherjee Dr Brejesh Lall SPCOM 2016 Department of Electrical Engineering Indian Institute of Technology Delhi Overview Intro. Road. . Networks. Renchu . Song, . . Weiwei . Sun, . . Fudan. University. Baihua Zheng, Singapore Management University. . Yu . Zheng, . Microsoft Research, . Beijing. Background. Big Data. Huge volume of spatial trajectories cause heavy burden to data storage and data process. Shuochao Yao, Yiwen Xu, Daniel Calzada. Network Compression and Speedup. 1. Source: . http://isca2016.eecs.umich.edu/. wp. -content/uploads/2016/07/4A-1.pdf. Network Compression and Speedup. 2. Why smaller models?. Póth Miklós. Fürstner Igor. Subotica Tech. Data. compression. Lossless - all original data can be recovered when the file is uncompressed. The signal is perfectly reconstructed from the available samples. (ZIP, GIF, PNG). Haar. Transform. 4c8 – . Dr.. David Corrigan. Entropy. It all starts with entropy. Calculating the Entropy of an Image. The entropy of . lena. is = 7.57 bits/pixel . approx. Huffman Coding. Huffman is the simplest entropy coding scheme. Vinay Raj Hampapur. Wendy Ni. Stanford University. March 8, 2011. Outline. Motivation. Description of our method. Results and comparisons. Achievements. Future work. Acknowledgement. References. 2. EE398A: Direction-Adaptive KLT for Image Compression. On the Noise Level Estimation. PROPOSAL. SPRING 2015. ADVISOR: Dr. . K.R.Rao. Presented by, . . . Komandla. . Sai. . Venkat. ,. UTA id: 1001115386. Variation.. Rachel W. Soares, Luciana R. Barroso, Omar A. S. Al-Fahdawi. ..  . Zachry Department of Civil Engineering-Texas A&M University. 3136 TAMU, 199 Spence Street, College Station, TX, 77843-3136, USA.. Swati . Singhal. . 1. Alan Sussman . The 2nd International Workshop on Data Reduction for Big Scientific . Data. UMIACS and Department of Computer Science. D. ata. reduction is growing concern for scientific computing. Outline. Need for Video Compression. Application Scenarios. Fundamentals of Video Coding. Redundancy Removal Techniques. Compression Artifacts. Encoding and Decoding Process Flow. Video Coding Standards. Mohammad Seyedzadeh. , Alex Jones, Rami . Melhem. University of Pittsburgh. 2. . DRCAT: Dynamically . Reconfigured . Counter based . Adaptive Tree . Deep-scaled . D. RAM . C. ells. DRAM . C. ells. Wordline. Gennady Pekhimenko. § . Vivek Seshadri. §. Onur Mutlu. . §. . Michael A. Kozuch. †. Phillip B. Gibbons. † . Todd C. Mowry. . §. § . Carnegie Mellon University . † . Intel Labs Pittsburgh. 1. Image Compression . Image compression involves reducing the size of image data file, while is retaining necessary information, the reduced file is called the compressed file and is used to reconstruct the image, resulting in the decompressed image. The original image, before any compression is performed, is called the uncompressed image file. The ratio of the original, uncompressed image file and the compressed file is referred to as the . 15 patients who underwent incision, drainage and compression by bandage. Group B constituted by 10 patients who underwent incision, drainage and compression by X-ray lms, the X-ray lms were Topics: . Diffy. , Morph, Gradient Compression. 3D CNNs. Used for video processing. Examining a series of F images in one step. T is typically 3. Note that F reduces as we advance (also because of pooling).