Lossless Compression of Color Filter Array Mosaic Images With Visualization via JPEG 2000

Hernández-Cabronero, Miguel; Marcellin, Michael W.; Blanes, Ian; Serra-Sagristà, Joan

doi:10.1109/tmm.2017.2741426

Cited by 25 publications

(17 citation statements)

References 33 publications

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“…M. Hernández-Cabronero et al describe in [9] a lossy and lossless CFA pattern compression based on JPEG 2000 which also allows simple reconstruction to an RGB image by merging parts of the demosaicking steps into the JPEG 2000 wavelet stage, creating a filter that is approximately identical to bi-linear interpolation. Color space conversion and conversion to a non-linear (gamma-corrected) colorspace are expressed by means of an ICC profile which is represented in the JPEG 2000 file format, though compression is performed in the linear domain.…”

Section: Related Workmentioning

confidence: 99%

Bayer CFA Pattern Compression With JPEG XS

Richter

Fobel

Descampe

et al. 2021

IEEE Trans. on Image Process.

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While traditional image compression algorithms take a full three-component color representation of an image as input, capturing of such images is done in many applications with Bayer CFA pattern sensors that provide only a single color information per sensor element and position. In order to avoid additional complexity at the encoder side, such CFA pattern images can be compressed directly without prior conversion to a full color image. In this paper, we describe a recent activity of the JPEG committee (ISO SC 29 WG 1) to develop such a compression algorithm in the framework of JPEG XS. It turns out that it is important to understand the "development process" from CFA patterns to full color images in order to optimize the image quality of such a compression algorithm, which we will also describe shortly. We introduce (1) a novel decorrelation step upfront processing (the so-called Star-Tetrix transform), along with (2) a pre-emphasis function to improve the compression efficiency of the subsequent compression algorithm (here, JPEG XS). Our experiments clearly indicate a gain over a RGB compression workflow in terms of complexity and quality (between 1.5dB and more than 4dB depending on the target bitrate). A comparison is also made with other state-of-the-art CFA compression techniques.

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Section: Related Workmentioning

confidence: 99%

Bayer CFA Pattern Compression With JPEG XS

Richter

Fobel

Descampe

et al. 2021

IEEE Trans. on Image Process.

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“…It reduces the band's cost of sending the image through a network with different formats of image. The most popular image formats are JPEG and JPEG-2000 [3,4], which are a prominent compression standard. There are broadly applied in numerous image applications and software.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient lossless encoder in image compression with low computation and low memory

Karthikeyan

Saravanakumar²,

Sivakumar

2021

IET Image Processing

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This research focuses on frequency-based lossless new encoding technique with low computation and low memory scheme. This encoding technique doesn't require, any table similar to Huffman and Golomb Rice encoder, and doesn't take high computation time like an arithmetic encoder. For better comparative results, nearly 200 standard images were tested with the proposed encoder and their results were compared with the standard encoders. Based on the analysis of results reported in the experimental part, the proposed scheme in Discrete Cosine Transformation (DCT) achieves a high Compression Ratio (CR) of 97.68 %, Bits Per Pixel (BPP) of 0.19, Peak Signal To Noise Ratio (PSNR) of 27.62 dB for pepper image, CR of 98.65%, BPP of 0.11 and PSNR of 29.24 dB for house image, CR of 98.58 %, BPP of 0.11, PSNR of 30.59 dB for pepper image, CR of 98.37%, BPP of 0.13, PSNR of 31.37dB for house image in Discrete Wavelet Transformation (DWT) than compared to other encoders. It consumes less computation time to attain superior CR with a better quality of reconstructed picture than DCT based Huffman, Golomb-Rice (GR) coder and Low Complexity, Low memory Entropy Coder (LLEC), DWT based Arithmetic, GR code, LLEC.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Hyperspectral image compression for example, is the essential concept in the remote sensing field not only to compress the bulk data, but also to store them in details [42][43][44][45][46][47]. A new standard, JPEG 2000, has also been developed to provide the framework of still image compression with wide-range of applications including remote sensing and medical imagery areas [48][49][50][51][52][53][54][55]. Combinations between the hyperspectral image compression with principal component analysis (PCA) and JPEG 2000 perform faster compression that are benefit for real-time applications [56][57][58][59][60][61][62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Ngeljaratan

Moustafa

2020

Sensors

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Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

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Lossless Compression of Color Filter Array Mosaic Images With Visualization via JPEG 2000

Cited by 25 publications

References 33 publications

Bayer CFA Pattern Compression With JPEG XS

Bayer CFA Pattern Compression With JPEG XS

Fast and efficient lossless encoder in image compression with low computation and low memory

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

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