A spatial extension of CIELAB for digital color‐image reproduction

Zhang, X.; Wandell, Brian A.

doi:10.1889/1.1985127

Cited by 591 publications

(381 citation statements)

References 5 publications

Supporting

Mentioning

356

Contrasting

Unclassified

Order By: Relevance

“…The first one was S-CIELAB (International Commission on Illumination) (Zhang and Wandell, 1996) and (Zhang et al, 1997). While the second test is the Peak Signal to Noise Ratio (PSNR) which can be computed using Eq.…”

Section: Resultsmentioning

confidence: 99%

Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

Al-Azawi¹,

Boussakta²,

Yakovlev³

2011

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Problem statement: Low complexity image compression algorithms are necessary for modern portable devices such as mobile phones, wireless sensor networks and high constraint power consumption devices. In such applications low bit rate along with an acceptable image quality are an essential requirements. Approach: This study proposes low and moderate complexity algorithms for colour image compression. Two algorithms will be presented; the first one is intensity based adaptive quantization coding, while the second is a combination of discrete wavelet transforms and the intensity based adaptive quantization coding algorithm. Adaptive quantization coding produces a good Peak Signal to Noise Ratio (PSNR), but with high bit rates compared with other low complex algorithms. The presented algorithms produce low bit rate whilst preserving the PSNR and image quality at an acceptable range. Results: Experiments were performed using different kinds of standard colour images, a multi level quantizer, different thresholds, different block sizes and different wavelet filters. Both algorithms considered the intensity variation of each colour plane. At high compression ratios the proposed algorithms produced 1-3 bpp bit rate reduction against the stand alone adaptive quantization coding for the same image quality. This reduction was achieved due to dropping of some blocks that claimed to be low intensity variation according to a comparison with predefined thresholds for each colour plane. The results show that the bit rate can be reduced by 72-88% for each low variation image block from the original bit rate. Conclusion: The results obtained show a good reduction in bit rate with the same PSNR, or a slightly less than PSNR of a standalone adaptive quantization coding algorithm. Further bit rate reduction has been achieved by decomposing the input image using different wavelet filters and intensity based adaptive quantization coding. The proposed algorithm comprises a number of parameters to control the performance of the compressed images.

show abstract

Section: Resultsmentioning

confidence: 99%

Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

Al-Azawi¹,

Boussakta²,

Yakovlev³

2011

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The results, which can be obtained from Experiments 1 and 2, are applicable to various purposes. Since CSFs have been widely used for evaluating image quality by predicting the perceptible differences between a pair of images (Barten, 1990;Daly, 1993;Zhang & Wandell, 1996;Wang & Bovik, 1996) surround luminance effects on CSF can be very useful for this application. Furthermore, the results can also be applied to simulate the appearance of a scene (Peli, 1996(Peli, , 2001) and evaluate the visual performance of the eye.…”

Section: Discussionmentioning

confidence: 99%

Portable LCD Image Quality: Effects of Surround Luminance

Kim¹

2011

Features of Liquid Crystal Display Materials and Processes

View full text Add to dashboard Cite

“…Output distortion maps state the probability for threshold discrimination of image differences at each pixel, and can be pooled to single figures using different techniques. Suprathreshold IF-IQMs such as S-CIELAB [19] and the modular image difference model [20] filter opponent color space images with CSFs or suprathreshold contrast discrimination models. JND calculations are generally performed using standard color difference/appearance models, which may account for local contrast detection and chromatic adaptation [20].…”

Section: Image Fidelity Metrics (If-iqm)mentioning

confidence: 99%