Improved BTC Using Luminance Bitmap for Color Image Compression

Wang, Jun; Min, Kyeong-Yuk; Jeung, Yeun-Cheul; Chong, Jong‐Wha

doi:10.1109/cisp.2009.5304208

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…RGB representations, however, are highly correlated, which implies that the RGB color space is not wellsuited for independent coding [29]. Since the human visual system is less sensitive to the position and motion of color than luminance [6,7]. Therefore, some color space conversions such as RGB to YCbCr are used [29,8].…”

Section: The Jpeg Compression Standardmentioning

confidence: 99%

“…Sequence of symbols and codes that are sent to the decoders. (5) 136 (7) 14 (9) 19 14 (9) 2 (10) BEGIN low = 0.0; high = 1.0; range = 1.0; while (symbol != terminator) { get (symbol); low = low + range * Range_low(symbol); high = low + range * Range_high(symbol); range = high -low; } output a code so that low <= code < high; END. [3] The Figure 4 depicts the flowchart of the arithmetic coding.…”

Section: Arithmetic Codingmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards

Shahbahrami¹,

Bahrampour²,

Rostami³

et al. 2011

IJCSEA

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Section: The Jpeg Compression Standardmentioning

confidence: 99%

Section: Arithmetic Codingmentioning

confidence: 99%

Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards

Shahbahrami¹,

Bahrampour²,

Rostami³

et al. 2011

IJCSEA

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show abstract

“…To the date, many variants are available in literature for BTC and its improvements. BTC is improved according to some error diffusion in [4] whereas the BTC for color image is improved in [3].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy edge detector based adaptive quantization image coding: FuzzAQC

Gambhir

Rajpal

2012

2012 1st International Conference on Recent Advances in Information Technology (RAIT)

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The new design method of an fuzzy edge detector based adaptive quantization image coding (fuzzAQC) is proposed. In this design first the image is decomposed to blocks and each block is AQC compressed according to their fuzzy classification as either edge or smooth blocks. This classification depends on Entropy Optimized Histogram based fuzzy edge detector. The block is coded with Bit plane, Quantization step and Block min or Block Mean. The experimental results shows that the proposed fuzzAQC scheme is superior to conventional AQC and Intensity based AQC on objective measures like MSE PSNR alongwith visual quality.

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“…An algorithm for colour image compression was presented in (Wang et al, 2009b). This algorithm generated a luminance bit map to represent the edge information, with three additional bits to represent the differences between luminance (Y) and the three channel colors.…”

Section: Introductionmentioning

confidence: 99%

Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

Al-Azawi¹,

Boussakta²,

Yakovlev³

2011

American Journal of Engineering and Applied Sciences

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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.

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Improved BTC Using Luminance Bitmap for Color Image Compression

Cited by 8 publications

References 11 publications

Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards

Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards

Fuzzy edge detector based adaptive quantization image coding: FuzzAQC

Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

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