Optimized information processing in resource-constrained vision systems. From low-complexity coding to smart sensor networks

Morbée, Marleen

doi:10.4995/thesis/10251/12126

Cited by 2 publications

(4 citation statements)

References 78 publications

(275 reference statements)

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“…In our experiments, we have assessed the results of eight grayscale images with 8 bits per sample, five of which (Zelda, Lena, Peppers, Barbara, and Baboon) with a resolution of 512 × 512 pixels and the rest, a full-HD (1080p) image, a 2, 048 × 2, 560 image, and a 4K UHD image. Furthermore, we have assigned values to the coding/decoding parameters (with N = 4, l 1 = l 2 = l 3 , and m 1 = m 2 = m 3 ) so as to obtain the best result on average of PSNR for a given bit-rate, although there may be other combinations of parameters that would optimize a particular image as proposed by Marleen Morbee in [19].…”

Section: Resultsmentioning

confidence: 99%

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MPCM: a hardware coder for super slow motion video sequences

Alcocer

López-Granado

Gutierrez

et al. 2013

EURASIP J. Adv. Signal Process.

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In the last decade, the improvements in VLSI levels and image sensor technologies have led to a frenetic rush to provide image sensors with higher resolutions and faster frame rates. As a result, video devices were designed to capture real-time video at high-resolution formats with frame rates reaching 1,000 fps and beyond. These ultrahigh-speed video cameras are widely used in scientific and industrial applications, such as car crash tests, combustion research, materials research and testing, fluid dynamics, and flow visualization that demand real-time video capturing at extremely high frame rates with high-definition formats. Therefore, data storage capability, communication bandwidth, processing time, and power consumption are critical parameters that should be carefully considered in their design. In this paper, we propose a fast FPGA implementation of a simple codec called modulo-pulse code modulation (MPCM) which is able to reduce the bandwidth requirements up to 1.7 times at the same image quality when compared with PCM coding. This allows current high-speed cameras to capture in a continuous manner through a 40-Gbit Ethernet point-to-point access.

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Section: Resultsmentioning

confidence: 99%

“…We take the strategy of dividing x [n 1 , n 2 ] into [19]. Then, the first one is encoded using PCM, removing l 0 LSBs, and the remaining parts using MPCM, removing l k LSBs and m k MSBs.…”

Section: Encoder Architecturementioning

confidence: 99%

MPCM: a hardware coder for super slow motion video sequences

Alcocer

López-Granado

Gutierrez

et al. 2013

EURASIP J. Adv. Signal Process.

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“…In [15], we developed a model for D when the interpolation error follows a Laplacian distribution. Hence, by solving (2) using the distortion model in [15], a GPCM encoder could obtain the optimum assignment for each block.…”

Section: Assignment Of Values To the Coding Parametersmentioning

confidence: 99%

“…Hence, by solving (2) using the distortion model in [15], a GPCM encoder could obtain the optimum assignment for each block. Nevertheless, since solving (2) is a complex task and encoding complexity is our main concern, the encoder uses a series of assignment rules (ARs) to perform the assignment.…”

Section: Assignment Of Values To the Coding Parametersmentioning

confidence: 99%

Generalized PCM Coding of Images

Prades-Nebot

Morbée

Delp

2012

IEEE Trans. on Image Process.

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Abstract-Pulse-code modulation (PCM) with embedded quantization allows the rate of the PCM bitstream to be reduced by simply removing a fixed number of least significant bits from each codeword. Although this source coding technique is extremely simple, it has a poor coding efficiency. In this paper, we present a generalized PCM (GPCM) algorithm for images that simply removes bits from each codeword. In contrast to PCM, however, the number and the specific bits that a GPCM encoder removes in each codeword depends on its position in the bitstream and the statistics of the image. Since GPCM allows the encoding to be performed with different degrees of computational complexity, it can adapt to the computational resources that are available in each application. Experimental results show that GPCM outperforms PCM with a gain that depends on the rate, the computational complexity of the encoding, and the degree of inter-pixel correlation of the image.EDICS. COM-LOC: Lossy coding of images and video.

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Optimized information processing in resource-constrained vision systems. From low-complexity coding to smart sensor networks

Cited by 2 publications

References 78 publications

MPCM: a hardware coder for super slow motion video sequences

MPCM: a hardware coder for super slow motion video sequences

Generalized PCM Coding of Images

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