Intensity-based dynamic speckle method using JPEG and JPEG2000 compression

Stoykova, Elena; Blagoeva, Blaga; Berberova-Buhova, Nataliya; Levchenko, Mikhail; Nazarova, Dimana; Nedelchev, Lian; Park, Joongki

doi:10.1364/ao.444831

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…This allowed us to propose coarse quantization [21] or binarization [22] as approaches for reducing volume of the speckle data. Furthermore, we proved efficiency of different JPEG compression formats applied to speckle images [23] although blocking or other artefacts were observed on the activity maps at low quality of decompressed images or high compression ratio.…”

Section: Introductionmentioning

confidence: 76%

Dynamic speckle imaging with SVD compression

Stoykova

Levchenko

Ivanov

et al. 2022

J. Phys.: Conf. Ser.

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Dynamic speckle imaging (DSI) of areas with different speed of processes ongoing in industrial or biological objects relies on statistical processing of a large number of images of the speckle patterns formed on the objects surface under laser illumination. The DSI visualizes the speed spatial distribution as an activity map. We propose compression of the raw DSI data by applying singular value decomposition (SVD). A specific feature of speckle images for DSI is lack of a structure with areas of close intensity values. The gain from the direct SVD application may be modest in cases when a great number of non-zero singular values is needed to build an activity map comparable in quality to the ground truth map from bitmap images. For higher compression, we propose SVD to be applied to the 2D arrays containing the differences between the successive images. The SVD compression has been verified by using synthetic and experimental data.

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Section: Introductionmentioning

confidence: 76%

Dynamic speckle imaging with SVD compression

Stoykova

Levchenko

Ivanov

et al. 2022

J. Phys.: Conf. Ser.

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“…Image degradation and artifacts caused by JPEG compression may be easily observed on the activity maps with resolution 1920x1080. Despite this visual drawback, JPEG standard does not critically change time correlation of speckle images 15 and even the low quality activity maps clearly show the change in activity during the experiment. It must be noted that for all sets of experiments, the time step was chosen equal to 300 ms since it determined the fastest rate at which the smartphone was able to transfer images at resolution 38402160 to the PC by Wi-Fi.…”

Section: Activity Mapsmentioning

confidence: 99%

Portable low-cost setup for outdoor implementation of dynamic speckle technique

Levchenko¹,

Stoykova²,

Hong³

et al. 2023

Optical Measurement Systems for Industrial Inspection XIII

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Dynamic speckle technique (DST) is based on speckle formation on the surface of objects illuminated with coherent light. Temporal speckle intensity fluctuations depend on the speed of micro-changes ongoing within the controlled objects. The DST visualizes as a set of 2D activity maps the temporal change of spatial speed distributions. In general, the DST set-up comprises components as a laser source with the required optics, vibration-isolated table, high-resolution camera and computer for data storage and image processing. Such setups are stationary, massive and relatively expensive, which decreases the number of possible DST biological or industrial applications. In this paper we propose a miniature portable device, based on a low-cost laser attached to a smartphone, and checked its efficiency under the field conditions. A strong argument for using a portable set-up is the fact that the absolute values of the speckle intensity are not needed to construct a reliable activity map. We proved this conclusion by numerical simulation of DST in noisy environment. We studied speckle patterns captured with a smartphone's camera. A personal computer (PC) was used for postprocessing o f speckle images. We conducted four sets of experiments. The raw data were recorded on the PC while the smartphone was connected as i) IP-camera and ii) USB-camera. In the third experiment, speckle images were captured and stored in the smartphone's memory. Data were transferred to the PC after the end of recording. To obtain ground truth activity maps, we repeated the experiment with the same object under laboratory conditions.

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“…These methods for image compression mainly consist of three components, namely, conversion, quantization, and entropy encoding. In methods such as JPEG and JPEG 2000, DCT or wavelet transforms use an input image and scalar-quantization coefficients to obtain compressed representations [15]. Entropy encoding schemes, Hoffmann, and adaptive arithmetic encoders are used to encode quantized coefficients for JPEG and JPEG 2000.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the proposed method, the bit rate per pixel (bpp), Mean Square Error (MSE), and Peak Signal to Noise ratio (PSNR) is used. These indicators are formulated in Equations ( 14), (15), and (16). In this equation, k is the size of the blocks and 𝑁 𝑐 is the number of codebooks:…”

Section: Evaluation Criteriamentioning

confidence: 99%

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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Image compression is one of the most attractive and practical topics. Without image compression, the image size becomes too large for storage. The transmission of uncompressing images on computer networks slows down and network bandwidth is wasted. Various approaches to image compression have been proposed so far, one of which is vector quantization using mathematical concepts and image processing to compress images. The LBG algorithm is a practical algorithm for compressing images using vector quantization concepts. Most researchers have used metaheuristic and optimization algorithms with a modeling approach of swarm behavior of living things to improve the quality of the LBG compression algorithm. This study uses a meta-heuristic method based on sine and cosine algorithms (SCA) to improve the quality of the image compression algorithm. In the proposed mathematical modeling method, the SCA algorithm is improved using spiral equations. The improved SCA algorithm is then used to find the optimal codebook in the LBG compression algorithm. Finding a better codebook in the proposed method will increase the quality of the compression images. The proposed method implemented in MATLAB software and experiments showed that the PSNR index in the proposed method improve the ratio of the LBG algorithm by about 13.73%. Evaluations show that the PSNR index of compressed images in the proposed method is higher and better than PBM, CS-LBG, FA-LBG, BA-LBG, HBMO-LBG, QPSO-LBG, PSO-LBG. The result shows the proposed method (or ISCA-LBG) has less time complexity than HHO and WOA compression algorithms.

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Intensity-based dynamic speckle method using JPEG and JPEG2000 compression

Cited by 8 publications

References 31 publications

Dynamic speckle imaging with SVD compression

Dynamic speckle imaging with SVD compression

Portable low-cost setup for outdoor implementation of dynamic speckle technique

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

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