Design of an Error Detection and Data Recovery Architecture for Motion Estimation Testing Applications

Cheng, Chang-Hsin; Liu, Yu; Hsu, Chun-Lung

doi:10.1109/tvlsi.2011.2109972

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…1 consists of two major blocks, i.e. error detection circuit (EDC) and data recovery circuit (DRC), to detect the errors and recover the corresponding data in a specific CUT [9]. The test code generator (TCG) in the architecture utilizes the concepts of RQ code to generate the corresponding test codes for error detection and data recovery.…”

Section: Proposed Edca Designmentioning

confidence: 99%

“…Fig. 2 shows the proposed EDCA circuit design for a specific PE i of a ME [9]. This architecture consists of blocks that generate the residue and quotient values that are used to detect the errors.…”

Section: Proposed Edca Designmentioning

confidence: 99%

“…The mathematical model of RQ code is simply described as follows. Assume that binary data X is expressed as [9]. Notably, ⎣ ⎦ i denotes the largest integer not exceeding i.…”

Section: B Residue-and-quotient Code Generationmentioning

confidence: 99%

“…The specific PE i estimates the absolute difference between the Cur_pixel of the search area and the Ref_pixel of the current macroblock. The SAD value for the macroblock with size of NxN can be evaluated: where r xij , q xij and r yij, q yij denote the corresponding RQ code of X ij and Y ij modulo m. Importantly, X ij and Y ij represents the luminance pixel value of Cur_pixel and Ref_pixel, respectively [9].…”

Section: Test Code Generationmentioning

confidence: 99%

“…Fig. 3 presents an example of pixel values of the Cur_pixel and Ref_pixel [9]. Based on (7), the SAD value of the 4x4 macroblock is …”

Section: F Sample Calculationmentioning

confidence: 99%

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Error Detection and Correction Architecture for Motion Estimation in video coding systems

Rukmani

Jagadeeswari

2013

2013 International Conference on Computer Communication and Informatics

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Motion Estimation (ME) is the process of determining the motion vectors that describe the transformation from one 2D image to other, usually from adjacent frames in a video sequence. The process of ME is the critical part of any video coding system as the video quality will be affected if an error has occurred in the ME. In order to test Motion Estimation in a video coding system an Error Detection and Correction Architecture (EDCA) is designed based on the Residue-and-Quotient (RQ) code. Multiple bit errors in the processing element (PE) i.e., key component of a ME, can be detected and the original data can be recovered effectively by using EDCA design. Experimental results indicate that the proposed design can detect multiple bit errors and effectively recover the data with reduced area. The area is reduced from 1665 to 345.

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Section: Proposed Edca Designmentioning

confidence: 99%

Section: Proposed Edca Designmentioning

confidence: 99%

“…The mathematical model of RQ code is simply described as follows. Assume that binary data X is expressed as [9]. Notably, ⎣ ⎦ i denotes the largest integer not exceeding i.…”

Section: B Residue-and-quotient Code Generationmentioning

confidence: 99%

Section: Test Code Generationmentioning

confidence: 99%

“…Fig. 3 presents an example of pixel values of the Cur_pixel and Ref_pixel [9]. Based on (7), the SAD value of the 4x4 macroblock is …”

Section: F Sample Calculationmentioning

confidence: 99%

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Error Detection and Correction Architecture for Motion Estimation in video coding systems

Rukmani

Jagadeeswari

2013

2013 International Conference on Computer Communication and Informatics

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Data Backups and Error Finding by Residue Quotient Code for Testing Applications

Pandeeswari¹,

Kabilan²,

Januanbumani³

et al. 2022

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)

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Activity-Based Resource Allocation for Motion Estimation Engines

Imran

Ashraf

Lee

et al. 2014

J CIRCUIT SYST COMP

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An architecture proof-of-concept which adapts the throughput datapath based on the anticipation of computational demand in dynamic environments is demonstrated and evaluated for a motion estimation (ME) engine. The input signal characteristics are exploited to anticipate the time varying computational complexity as well as to instantiate dynamic replicas (DRs) to realize fault-resilience. The scheme employs amorphous processing elements (APEs) which either perform as active elements (AEs) to maintain quality/throughput, serve as DRs to increase reliability levels, or hibernate passively as reconfigurable slack (RS) available to other tasks. Experimental results from a hardware platform for field programmable gate array (FPGA)-based video encoding demonstrate power efficiency and fault-tolerance of the ME engine. A significant reduction in power consumption is achieved ranging from 83% for low-motion-activity scene's to 12.5% for high motion activity video scenes. The scenes motion activity is utilized to improve redundancy for the purpose of priority based diagnosis of the computing modules. In addition, a graceful degradation strategy is developed to recover from hard errors by adapting the search range of candidate motion vectors (MVs). This adaptive hardware scheme is shown to automatically demote the faulty resources in FPGA devices based on streaming performance.

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Design of an Error Detection and Data Recovery Architecture for Motion Estimation Testing Applications

Cited by 3 publications

References 22 publications

Error Detection and Correction Architecture for Motion Estimation in video coding systems

Error Detection and Correction Architecture for Motion Estimation in video coding systems

Data Backups and Error Finding by Residue Quotient Code for Testing Applications

Activity-Based Resource Allocation for Motion Estimation Engines

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