PDF-Bernd Girod EEb Image and Video Compression Motion Compensation no

1 Overview motion compensated coding Motion compensated prediction Motion compensated hybrid coding Power spectral density of the motion compensated prediction error Rate distortion analysis Loop filter Motion compensation with sub pel accuracy Bern. Smash the information to bits. Overview. The primary goal of image compression is to minimize the memory footprint of image data so that storage and transmission times are minimized. . Storage capacity can be limited, as is the case with digital cameras. Introduction. Compression is the reduction in size of data in order to save space or transmission time. . Compression is the process of reducing the size of a file by encoding its data information more efficiently . Ivan . Dimitrov. 508/2012. . Photography. Fast . moving object or a longer exposure time may result in blurring . artifacts . The same effect can be achieved in a artificial way via image . procesing. 2-D Warping and Block Matching. Shinjini. . Kundu. Anand. . Kamat. . Tarcar. EE398A Final Project. 1. EE398A - Compression of Light Fields using 2-D Warping and Block Matching. Outline. Motivation and . 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. Organized by Anna . K. Monfils and . Ann . K. Sakai . BSA Human Diversity . Committee. Sponsors. iDigBio. Ecology Section . of Botanical Society of . America. Teaching Section . of Botanical Society of . David Atkinson:. I have no . relevant financial interest or relationship . to disclose with regard to the subject matter of this presentation.. Motion Compensation Strategies. David Atkinson. . . D.Atkinson@ucl.ac.uk. 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. Outline. Need for Video Compression. Application Scenarios. Fundamentals of Video Coding. Redundancy Removal Techniques. Compression Artifacts. Encoding and Decoding Process Flow. Video Coding Standards. In the Compensation Training, we will discuss the details on ANR’s Compensation strategies and how it relates to new hires, merits, and other employment issues.. What the compensation process entails. Edward . Reuss. Co-chair SMPTE Technical Committee TC-10E Essence. Agenda. High Level Concepts. Production & Post Workflows vs. Consumer Distribution. Low Resolution Chroma Channels. Image Transformation. 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 .