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UT Arlington EE 5359 - DIRAC VIDEO CODEC

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THESIS PROPOSALTHESIS PROPOSAL - Aruna Ravi Student I.D.: 1000-565191 Date : May 15, 2009 1PERFORMANCE ANALYSIS OF DIRAC VIDEO CODECPROPOSAL: The objective of this thesis is to implement Dirac video codec (encoder anddecoder) [1] based on several input test sequences, and compare its performance with H.264 /MPEG-4 Part 10 AVC. [13 - 16]OVERVIEW OF THE DIRAC CODEC:Dirac is a hybrid motion-compensated state-of-the-art video codec that uses modern techniquessuch as wavelet transforms and arithmetic coding [1]. It is an open technology , designed toavoid patent infringement and can be used without the payment of license fees. It is well suitedto the business model of public service broadcasters as it can be easily recreated for newplatforms. Dirac is aimed at applications ranging from HDTV (high definition television) to webstreaming.There are two parts in the Dirac development process: (A) a compression specification for the bitstream and decoder, and (B) software for compression and decompression. The software is notintended simply to provide reference coding and decoding - It is a prototype implementation thatcan freely be modified and deployed. The decoder in particular is designed to be fast and moreagile. The resulting specification is simple and straightforward to implement and optimize forreal-time performance. [1]Architecture: In the Dirac codec, image motion is tracked and the motion information is used tomake a prediction of a later frame. A transform is applied to the prediction error between thecurrent frame and the previous frame aided by motion compensation and the transformcoefficients are quantized and entropy coded. [1] The encoder has the architecture as shown inFig(1), whilst the decoder performs the inverse operations (Fig(2)).2Fig(1). Encoder architecture (the decoder performs the inverse operations) [1] [2]Notations: Vin : Input video frame; P: Previous frame (Motion compensated) : Motioncompensated prediction error(MCPE): e = Vin – P; eTQ : MCPE after wavelet transformation andquantization; e’ : MCPE after inverse transformation; Vlocal = P + e’ Fig(2). Decoder architectureNotations: P: Motion compensated previous frame, e: Motion compensated prediction error(MCPE): Prediction error after inverse wavelet transformation and quantization; Vout: Outputvideo frame3Temporal and spatial redundancies are removed by motion estimation , motion compensation anddiscrete wavelet transform respectively. Dirac uses a more flexible and efficient form of entropycoding called arithmetic coding which packs the bits efficiently into the bit stream. [1]Wavelet Transform: The 2D discrete wavelet transform provides Dirac with the flexibility tooperate at a range of resolutions. This is because wavelets operate on the entire picture at once,rather than focusing on small areas at a time. The wavelet transform is constructed by repeatedfiltering of signals into low- and high-frequency parts. For two-dimensional signals, this filteringoccurs both horizontally and vertically. At each stage, the low horizontal / low vertical frequencysub-band is split further, resulting in logarithmic frequency decomposition into sub-bands. Wavelet transforms have been proven to provide a more efficient technique than blocktransforms with still images. Within the Dirac wavelet filters, the data is encoded in 3 stages asshown in Fig(3). Daubechies wavelet filters are used to transform and divide the data in sub-bands which then are quantized with the corresponding RDO (rate distortion optimization)parameters and then variable length encoded. These three stages are then reversed at the decoder.[5] Fig(3). Dirac wavelet transform architecture [5]The transform packs most of the information into only a few sub-bands (at low frequency) asshown in Fig(4), which allows compression to be achieved. This process can be repeated toachieve higher levels of wavelet transform (Fig(5)). A Dirac-coded picture is free from blockartifacts and is clearly superior in the case of moving images. [1] Fig(4). Stages of wavelet transform [1]4Fig(5). Wavelet transform frequency decomposition [5] Scaling and Quantization: Scaling involves taking frame data after application of wavelettransform and scaling the coefficients to perform quantization. Quantization employs a ratedistortion optimization algorithm to strip information from the frame data that results in as littlevisual distortion as possible. Dirac uses a dead-zone quantization as shown in Fig(6) whichdiffers from orthodox quantization by making the first set of quantization step twice as wide.This allows Dirac to perform coarser quantization on smaller values. [5] Fig(6). Dead-zone quantizer with quality factor (QF) [5]Entropy Coding: This is applied after wavelet transform to minimize the number of bits used. Itconsists of three stages: binarization, context modeling and arithmetic coding as shown in Fig(7).The purpose of the first stage is to provide a bit stream with easily analyzable statistics that canbe encoded using arithmetic coding, which can adapt to those statistics, reflecting any localstatistical features. The context modeling in Dirac is based on the principle that whether acoefficient is small or not is well-predicted by its neighbors and its parents.[3] Arithmetic codingperforms lossless compression and is both flexible and efficient.5Fig(7). Dirac entropy coding architecture [6] The non-zero values in the higher frequency sub-bands of the wavelet transform are often in thesame part of the picture as they are in lower frequency sub-bands. Dirac creates statistical modelsof these correlations and arithmetic coding allows us to exploit these correlations to achievebetter compression. The motion information estimated at the encoder also uses statisticalmodeling and arithmetic coding to compress it into the fewest number of bits. This compresseddata is put into the bit stream, to be used by the decoder as part of the compressed video.Motion Estimation: Exploits temporal redundancy in video streams by looking for similaritiesbetween adjacent frames. Dirac implements hierarchical motion estimation (Fig(8)) in


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UT Arlington EE 5359 - DIRAC VIDEO CODEC

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