&lt;title&gt;Spatial exemplars and metrics for characterizing image compression transform error&lt;/title&gt;

Mathematics of Data/Image Coding, Compression, and Encryption IV, With Applications

Ritter

Caimi

2001

Self Cite

A crucial deficiency of lossy blockwise image compression is the generation of local artifacts such as ringing defects, obscuration of fine detail, and blocking effect (BE). To date, few published reports of image quality measures (IQMs) have addressed the detection of such errors in a realistic, efficient manner. Exceptions are feature-based IQMs, perceptual IQMs, error detection templates, and quantification of BE that support its reduction in JPEG-and wavelet-compressed imagery.In this paper, we present an enhanced suite of IQMs that emphasize detection of local, feature-specific errors that corrupt visual appearance or numerical integrity of decompressed digital imagery. By the term visual appearance is meant subjective error, in contrast with objectively quantified effects of compression on individual pixel values and their spatial interrelationships. Subjective error is of key importance in human viewing applications, for example, Internet video.Objective error is primarily of interest in object recognition applications such as automated target recognition (ATR), where implementational concerns involve the effect of compression or decompression algorithms on probability of detection (Pd) and rate of false alarms (Rfa).Analysis of results presented herein emphasizes application-specific quantiication of local compression errors. In particular, introduction of extraneous detail (e.g., ringing defects or BE) or obscuration of source detail (e.g., texture masking) adversely impact both subjective and objective error of a decompressed image. Blocking effect is primarily a visual problem, but can confound ATh filters when a target spans a block boundary. Introduction of point or cluster errors primarily degrades ATR filter performance, but can also produce noticeable degradation of fine detail for human visual evaluation of decompressed imagery. In practice, error and performance analysis is supported by examples of ATR imagery including airborne and underwater mono-and multi-spectral images.

Section: Preliminary Resultssupporting

confidence: 60%

<title>Local analysis of distortion and ATR error associated with decompressed digital imagery</title>

Mathematics of Data/Image Coding, Compression, and Encryption IV, With Applications

Ritter

Caimi

2001

Self Cite

“…Here, F could mathematically describe a filter that is designed to enhance regions of interest. The computation of F as a region enhancement algorithm A implemented on a sequential workstation H can yield different numerical results (and, possibly, different detection results) if A is based on wavelet technology versus spatial edge detection [7]. Differences in numerical results might also be obtained when A is computed on a SIMD-parallel array versus a pipelined digital signal processor.…”

Section: Theory Of Forward Error Analysismentioning

confidence: 95%

“…As such, the primary objective of this paper is to provide image processing engineers and algorithm designers with an overview of previous and existing technology that can serve as a foundation for enhancing the accuracy of existing image processing algorithms. Our secondary objective is to support the combination of bit accounting techniques with statistical methods for forward analysis that we have published previously [2][3][4]6,7], to form a basis for multi-level abstraction in future analysis implementations.…”

Section: Phenomenology and Previous Workmentioning

confidence: 99%

Precise accounting of bit errors in floating-point computations

Mathematics for Signal and Information Processing

2009

Self Cite

Floating-point computation generates errors at the bit level through four processes, namely, overflow, underflow, truncation, and rounding. Overflow and underflow can be detected electronically, and represent systematic errors that are not of interest in this study. Truncation occurs during shifting toward the least-significant bit (herein called rightshifting), and rounding error occurs at the least significant bit. Such errors are not easy to track precisely using published means. Statistical error propagation theory typically yields conservative estimates that are grossly inadequate for deep computational cascades. Forward error analysis theory developed for image and signal processing or matrix operations can yield a more realistic typical case, but the error of the estimate tends to be high in relationship to the estimated error.In this paper, we discuss emerging technology for forward error analysis, which allows an algorithm designer to precisely estimate the output error of a given operation within a computational cascade, under a prespecified set of constraints on input error and computational precision. This technique, called bit accounting, precisely tracks the number of rounding and truncation errors in each bit position of interest to the algorithm designer. Because all errors associated with specific bit positions are tracked, and because integer addition only is involved in error estimation, the error of the estimate is zero.The technique of bit accounting is evaluated for its utility in image and signal processing. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm being analyzed, and its error estimation algorithm. Because of the significant overhead involved in error representation, it is shown that bit accounting is less useful for real-time error estimation, but is well suited to analysis in support of algorithm design.

“…As shown in [31][32][33], the vast majority of error measures for image compression are based in one way or another on MSE, which does not necessarily describe subtleties such as feature orientation errors, or the effect of feature distortions on the syntactic or semantic perception of an object within the context of a scene. Compromises in the complexity/accuracy tradeoff have been reported in [25,34,35], where multispectral image properties are employed to yield a small residual color error and coarse motion information that is sufficiently accurate to satistfy application-specific constraints.…”

Section: Alternative Approachesmentioning

confidence: 99%

Detection and Characterization of Motion in Video Compression

Ritter

2003

SPIE Proceedings

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The movement of objects in video sequences comprises a type of spatiotemporal redundancy that can be decreased mathematically to facilitate video compression. This observation holds particularly in the case of periodic motion, for example, bipedal or quadrupedal locomotion or repetitive gestures. Previously-published motion detection techniques were based on optical flow, interframe differences represented in terms of transform coefficient perturbations, or changes in eigenvalues between frames in a video sequence. However, such methods have deficits that include sensitivity to noise, burdensome computational requirements (e.g., floating point operations), and prohibitive instability in the presence of spatial or temporal interframe discontinuities.In this paper, we discuss several techniques of motion detection in two-dimensional images of three-dimensional scenespointwise tracking of constant-intensity pixels, region-based vector field characterization of apparent motion, and correlationbased detection. In the latter category is a technique called Interframe Similarity Matrices (ISMs). ISMs were developed and successfully applied by Yacoob, Black, and Davis [1,2] to address the challenging problem of detecting human and animal motion in surveillance video sequences. In particular, given an N-frame video sequence, an NxN-element interframe correlation matrix can be constructed and Fourier-transformed to obtain an N/2-element power spectrum of interframe periodicities. Different actions (e.g., walking vs. running) and various actors (e.g., quadruped versus human) tend to be characterized by distinct spatiotemporal spectra, and can often be distinguished from one another.Since each spectrum can be computed from a sequence of small image regions, it is possible to represent interframe motion by a pixel tagging technique, thus implementing detection, segmentation, and representation. If there are K objects with M pixels per frame having B bits per pixel (bpp) in N frames of a compressed video sequence, and each object is segmented into a region represented by a P bit tag, then increased compression results if NKMB < NKP, i.e., MB < P. Implementational discussion concerns efficient algorithms for tagging of motion-containing regions, to decrease the representational overhead to several bits per region. We also discuss motion encoding in a compressed format for purposes of efficient extraction of motion parameters from a compressed image, which can support efficient object recognition in highresolution compressed image sequences.