Incremental refinement of DFT and STFT approximations

Winograd, Joseph M.; Nawab, S. Hamid

doi:10.1109/97.365530

Cited by 18 publications

(6 citation statements)

References 6 publications

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Section: Introductionmentioning

confidence: 99%

“…Finally, existing image processing realizations produce an "all or nothing" output: one cannot interrupt the computation when system resources suddenly become unavailable (or when delay constraints are about to be violated) and retrieve a meaningful approximation of the final result 1 . An exception is found in proposals for incremental computation of transforms and salient point detection algorithms [11] [12], where the main principle is: under a refinement of the source description, the computation of the image processing task refines the previously-computed result. However, existing work [11] [12] is using arithmetic complexity estimates and no practical realizations are given.…”

Section: Introductionmentioning

confidence: 99%

“…An exception is found in proposals for incremental computation of transforms and salient point detection algorithms [11] [12], where the main principle is: under a refinement of the source description, the computation of the image processing task refines the previously-computed result. However, existing work [11] [12] is using arithmetic complexity estimates and no practical realizations are given.In this paper we address this aspect by proposing a practical software framework for image processing tasks exhibiting incremental refinement of computation. Our software designs of two-dimensional (2D) convolution and block-matching operations combine incremental computation with a recentlyproposed packing approach that enables the calculation of multiple limited-dynamic-range integer operations via one 32-bit or 64-bit arithmetic operation.…”

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confidence: 99%

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Software Designs of Image Processing Tasks With Incremental Refinement of Computation

Anastasia

Andreopoulos

2010

IEEE Trans. on Image Process.

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We propose software designs that perform incremental computation with monotonic distortion reduction for twodimensional convolution and frame-by-frame block-matching tasks. In order to reduce the run time of the proposed designs, we combine bitplane-based computation with a packing technique proposed recently. In the case of block matching, we also utilize previously-computed motion vectors to perform localized search when incrementing the precision of the input video frames. The applicability of the proposed approach is demonstrated by execution time measurements on the xo-laptop ("100$ laptop") and on a mainstream laptop; our software is also made available online. In comparison to the conventional (non-incremental) software realization, the proposed approach leads to scalable computation per input frame while producing identical (or comparable) precision for the output results of each operating point. In addition, the execution of the proposed designs can be arbitrarily terminated for each frame with the output being available at the already-computed precision.Index Terms-complexity-scalable image processing, incremental refinement of computation, programmable processors INTRODUCTIONSeveral popular applications, such as media players, image and video post-processing, and motion estimation and compensation, are being implemented today via software solutions in general-purpose processors. New generations of processors are increasingly powerful and enable more dedicated resource allocation to such real-time multimedia tasks due to multicore designs [1]. At the same time, new generations of software compilers now automatically generate platform-specific optimized assembly code from C++ code [2], thereby enabling platform-independent C++ software solutions to achieve high processor utilization factors. Existing algorithmic-oriented research focuses on complexity reduction [3]- [5] or complexity scalability for image processing tasks [6]-[8], where computational complexity is decreased and approximate results are produced. Implementation-oriented research focuses on multimediadriven energy scaling of processors via dynamic voltage scaling [9] [10] in an attempt to provide computational scalability with approximate results. However, previous approaches can only obtain one operational point in the complexity-distortion curve [3] [7], without being able to increment the quality of the output with increased computation. In addition, in practical image and video coding systems, complexity does not scale down significantly with decreased source precision (decreased bitrate) [7]. Finally, existing image processing realizations produce an "all or nothing" output: one cannot interrupt the computation when system resources suddenly become unavailable (or when delay constraints are about to be violated) and retrieve a meaningful approximation of the final result1 . An exception is found in proposals for incremental computation of transforms and salient point detection algorithms [11] [12], where the main principle is:...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Software Designs of Image Processing Tasks With Incremental Refinement of Computation

Anastasia

Andreopoulos

2010

IEEE Trans. on Image Process.

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“…That work has recently been extended to include a class of multi-stage algorithms for incremental D F T and STFT refinement [6]. This new approach This work was sponsored in part by the Department of the Navy, Office of the Chief of Naval Research, contract number N00014-93-1-0686 as part of the Advanced Research Projects Agency's RASSP program.…”

Section: Deadline-based Algorithmsmentioning

confidence: 98%

“…We also assume that j , is independent of i. The quality/cost points that satisfy both equation (9) and the design constraints are then determined analytically, and the corresponding (continuous) values of cz, T,, and ut are found using equations (4)- (6). These results are then rounded appropriately to integral values which, through equation ( 2 ) , define the resulting incremental refinement algorithm.…”

Section: Algorithm Designmentioning

confidence: 99%

Approximate signal processing using incremental refinement and deadline-based algorithms

Nawab

Winograd

1995 International Conference on Acoustics, Speech, and Signal Processing

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A framework for approximate signal processing is introduced which can be used to design novel classes of algorithms for performing D F T and S T F T calculations. In particular, we focus on the derivation of multi-stage incremental refinement algorithms that meet a variety of design criteria on the tradeoff achieved at each stage between solution quality and computational cost.can be used to generate a large number of algorithms whose stages possess different cost versus quality tradeoff characteristics. In this paper, we demonstrate the application of a mathematical framework for quality and cost assessment to the design of incremental D F T refinement algorithms whose tradeoffs meet a variety of design criteria. Since the S T F T can be decomposed into a series of D F T calculations, these design techniques may be applied to the derivation of incremental S T F T refinement algorithms as well.

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Probabilistic complexity analysis for a class of approximate DFT algorithms

Winograd

Nawab

1996

J VLSI Sign Process Syst Sign Image Video Technol

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Incremental refinement of DFT and STFT approximations

Cited by 18 publications

References 6 publications

Software Designs of Image Processing Tasks With Incremental Refinement of Computation

Software Designs of Image Processing Tasks With Incremental Refinement of Computation

Approximate signal processing using incremental refinement and deadline-based algorithms

Probabilistic complexity analysis for a class of approximate DFT algorithms

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