A subband coding technique for image compression in single CCD cameras with Bayer color filter arrays

Toi, Takeshi; Ohita, M.

doi:10.1109/30.754434

Cited by 51 publications

(32 citation statements)

References 3 publications

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“…This behavior can be attributed to redundancy introduced by the interpolation performed during the development process, which is not completely removed during compression. This is consistent with previous work [20]- [27], [29]- [36], [45].…”

Section: ) Real Mosaic Imagessupporting

confidence: 83%

“…While these algorithms are suitable for use on real mosaic images, in many cases, results have been reported only for simulated mosaic images. These simulated mosaic images were obtained by downsampling fully developed 8-bit sRGB images [20]- [22], [26]- [28], [35], [36], [45]. This approach is reasonable, but not ideal since the development process described in Section II cannot be inverted perfectly [10], [30].…”

Section: A Lossless Compression Performancementioning

confidence: 99%

“…These approaches offer fast implementations, but at the cost of reduced compression ratios compared to state-of-the-art algorithms for mosaic images from the literature. Such algorithms include those based on subband coding [20], [21], JPEG [22]- [25], JPEG-LS [26], [27], JPEG-XT [28], JPEG 2000 [26], [29]- [32], vector quantization [33], [34] or Golomb-Rice codes [35], [36].…”

Section: Introductionmentioning

confidence: 99%

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

Hernández-Cabronero

Marcellin

Blanes

et al. 2018

IEEE Trans. Multimedia

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Abstract-Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images, which must be "developed" (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the "development process" using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible, it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Data compression is thus of interest. Several compression methods for mosaic images have been proposed in the literature. However, they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper, a novel compression pipeline is proposed that removes these requirements. Specifically, mosaic images can be losslessly recovered from the resulting compressed files, and, more significantly, images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality, while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

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Section: ) Real Mosaic Imagessupporting

confidence: 83%

Section: A Lossless Compression Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hernández-Cabronero

Marcellin

Blanes

et al. 2018

IEEE Trans. Multimedia

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“…Compressing directly the CFA data rather than the demosaicked image basically results in a three-fold reduction of the information to be coded [10]- [13]. Moreover, decompressing the CFA image in its original quality or with a small compression error allows to obtain a high-quality demosaicked image by running computationally expensive, sophisticated demosaicking algorithms using a companion PC.…”

Section: Cfa Image Compressionmentioning

confidence: 99%

Single-Sensor Image Compression from the End-User's Perspective

Lukáč

Plataniotis

2006

2006 Canadian Conference on Electrical and Computer Engineering

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“…Differential Pulse Code Modulation (DPCM) [4]. More sophisticated methods for Bayer compression are based on Wavelet transform ( [5], [6]) and JPEG ( [7], [8]). A vector quantization technique for CFA images encoding has been recently presented [9], [3].…”

Section: Introductionmentioning

confidence: 99%