Mapping Stream Programs into the Compressed Domain

Due to the high data rates involved in audio, video, and signalprocessing applications, it is imperative to compress the data todecrease the amount of storage used. Unfortunately, this implies thatany program operating on the data needs to be wrapped by adecompression and re-compression stage. Re-compression can incursignificant computational overhead, while decompression swamps theapplication with the original volume of data.In this paper, we present a program transformation that greatlyaccelerates the processing of compressible data. Given a program thatoperates on uncompressed data, we output an equivalent program thatoperates directly on the compressed format. Our transformationapplies to stream programs, a restricted but useful class ofapplications with regular communication and computation patterns. Ourformulation is based on LZ77, a lossless compression algorithm that isutilized by ZIP and fully encapsulates common formats such as AppleAnimation, Microsoft RLE, and Targa.We implemented a simple subset of our techniques in the StreamItcompiler, which emits executable plugins for two popular video editingtools: MEncoder and Blender. For common operations such as coloradjustment and video compositing, mapping into the compressed domainoffers a speedup roughly proportional to the overall compressionratio. For our benchmark suite of 12 videos in Apple Animationformat, speedups range from 1.1x to 471x, with a median of 15x

The MoCA Workbench: Support for Creativity in Movie Content Analysis

Semantic access to the content of a video is highly desirable for multimedia content retrieval. Automatic extraction of semantics requires content analysis algorithms. Our MoCA (Movie Content Analysis) project provides an interactive workbench supporting the researcher in the development of new movie content analysis algorithms. The workbench offers data management facilities for large amounts of video/audio data and derived parameters. It also provides an easy-to-use interface for a free combination of basic operators into more sophisticated operators. We can combine results from video track and audio track analysis. The MoCA Workbench shields the researcher from technical details and provides advanced visualization capabilities, allowing attention to focus on the development of new algorithms. The paper presents the design and implementation of the MoCA Workbench and reports practical experience

Parallel processing of streaming media on heterogeneous hosts using work stealing

Master'sMASTER OF SCIENC

Language and compiler support for stream programs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-166).Stream programs represent an important class of high-performance computations. Defined by their regular processing of sequences of data, stream programs appear most commonly in the context of audio, video, and digital signal processing, though also in networking, encryption, and other areas. Stream programs can be naturally represented as a graph of independent actors that communicate explicitly over data channels. In this work we focus on programs where the input and output rates of actors are known at compile time, enabling aggressive transformations by the compiler; this model is known as synchronous dataflow. We develop a new programming language, StreamIt, that empowers both programmers and compiler writers to leverage the unique properties of the streaming domain. StreamIt offers several new abstractions, including hierarchical single-input single-output streams, composable primitives for data reordering, and a mechanism called teleport messaging that enables precise event handling in a distributed environment. We demonstrate the feasibility of developing applications in StreamIt via a detailed characterization of our 34,000-line benchmark suite, which spans from MPEG-2 encoding/decoding to GMTI radar processing. We also present a novel dynamic analysis for migrating legacy C programs into a streaming representation. The central premise of stream programming is that it enables the compiler to perform powerful optimizations. We support this premise by presenting a suite of new transformations. We describe the first translation of stream programs into the compressed domain, enabling programs written for uncompressed data formats to automatically operate directly on compressed data formats (based on LZ77). This technique offers a median speedup of 15x on common video editing operations.(cont.) We also review other optimizations developed in the StreamIt group, including automatic parallelization (offering an 11x mean speedup on the 16-core Raw machine), optimization of linear computations (offering a 5.5x average speedup on a Pentium 4), and cache-aware scheduling (offering a 3.5x mean speedup on a StrongARM 1100). While these transformations are beyond the reach of compilers for traditional languages such as C, they become tractable given the abundant parallelism and regular communication patterns exposed by the stream programming model.by William Thies.Ph.D

RIVL: A Resolution Independent Video Language

This paper describes one such language, called Rivl (pronounced "Rival"). In Rivl, video operations are expressed independent of the internal representation of video data. Just as it is the responsibility of traditional languages to map a floating-point operation onto the underlying bit manipulations, it is Rivl's responsibility to map image and video clip operations onto the underlying pixel and frame manipulations

Optimal Parallel MPEG Encoding

Moving Picture Expert Group (MPEG) encoding is a computationally expensive operation. This paper outlines a method of compressing video streams from within a Resolution Independent Video Language (RIVL) using the MPEG standard of compression in parallel. Two processes, a client and a server, work in tandem to parallelize the MPEG encoding process. The client dispatches jobs to each of the available servers for processing and the servers handle the compression of the video streams. The goal of Optimal Parallel MPEG Encoding is to derive a scalable, robust protocol to efficiently handle this computation. 1. INTRODUCTION We implemented an algorithm which encodes MPEG[8] videos using a network of workstations on a shared file system. To accomplish this, the video to be encoded is divided at logical breaks in the video stream. These breaks are determined either by use of an existing cut detector or an automatic step size calculation. The major advantage of this implementation is increased p..