• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.9-11
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• 2006

This paper presents JPEG quantization table design using RD optimization and neural network. Using R-D optimization, quantization table with good performance can be obtained. However, it is time-consuming and difficult to adopt to embedded systems. In this paper, a new quantization table design method is proposed using R-D optimization and neural network. Neural network learns the quantization table obtained from R-D optimization and produces a quantization table for the Images. The proposed system is applied to Yale face data. From the simulation results, it has been shown that the proposed codec has better performance than JPEG.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.453-456
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• 2004

Due to the bandwidth and storage limitations medical images are needed to be compressed before transmission and storage. DICOM (Digital Imaging and Communications in Medicine) specification, which is the medical images standard, provides a mechanism for supporting the use of JPEG still image compression standard. In this paper, we explain a method for compressing medical images by PEG standard and propose two methods for JPEG compression. First, because medical images differ from natural images in optical feature, we propose a method to design adaptively the quantization table using spectrum analysis. Second, because medical images have higher pixel depth than natural images do, we propose a method to design Huffman table which considers the probability distribution feature of symbols. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.19-24
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• 2008

This paper presents a new JPEG quantization table design method for mobile images in handset. From the characteristics of the mobile images, we propose a modeling method of the quantization table and select the optimized pre-emphasis factor from various sets of $240{\times}320$ images. From the experiment, we show that the performance is improved in the sense of bpp and PSNR, applying the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.824-833
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• 2004

Due to the bandwidth and storage limitations, medical images are needed to be compressed before transmission and storage. DICOM (Digital Imaging and Communications in Medicine) specification, which is the medical images standard, provides a mechanism for supporting the use of JPEG still image compression standard. In this paper, we explain a method for compressing medical images by JPEG standard and propose two methods for JPEG compression. First, because medical images differ from natural images in optical feature, we propose a method to design adaptively the quantization table using spectrum analysis. Second, because medical images have higher pixel depth than natural images do, we propose a method to design Huffman table which considers the probability distribution feature of symbols. Therefore, we propose methods to design a quantization table and Huffman table suitable for medical images. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard. Proposed methods which are satisfied JPEG Standard, can be applied to PACS (Picture Archiving and Communications System).

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.561-564
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• 2005

In this paper, we present a new JPEG (Joint Photograph Experts Group) compression architecture which compresses still image into fixed size of bitstream to use restricted system memory efficiently. The size of bitstream is determined by the complexity of image and the quantization table. But the quantization table is set in advance the complexity of image is the essential factor. Therefore the size of bitstream for high complexity image is large and the size for low complexity image is small. This means that the management of restricted system memory is difficult. The proposed JPEG encoder estimates the size of bitstream using the correlation between consecutive frames and selects the quantization table suited to the complexity of image. This makes efficient use of system memory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.794-806
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• 2004

This paper deals with correction of frequency offset and analysis of quantization angle noise in the IEEE 802.1la OFDM system. The rotation phase per symbol due to the carrier frequency offset is estimated from auto-correlation of the short Preambles, which are over-sampled for the reduction of noise in OFDM signals. The pilot signals are introduced to estimate the rotation phase per OFDM symbol due to estimation error of the carrier frequency offset and the sampling frequency onset. During the estimation and correction of the frequency onsets, a CORDIC processor and a look-up table are used for the conversion between a rotation phase and its complex number. Being calculated by a limited number of bits in the CORDIC processor and the look-up table, the rotation phase and its complex number have quantization angle errors. The quantization errors are analyzed as SNR (signal to noise ratio) due to the quantization bit numbers. The minimum bit number is suggested to meet the specification of IEEE 802.1la properly. Finally, the quantization errors are evaluated through simulations on number of quantization bits and SNR of received signals.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.187-196
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• 2017

The size of raw data has dramatically increased due to the recent trend of Synthetic Aperture Radar(SAR) development plans for high resolution and high definition image acquisition. The large raw data has an impact on satellite operability due to the limitations of storage and transmission capacity. To improve the SAR operability, the SAR raw data shall be compressed before transmission to the ground station. The Block Adaptive Quantization (BAQ) algorithm is one of the data compression algorithm and has been used for a long time in the spaceborne SAR system. In this paper, an optimization method of BAQ threshold table is introduced using real SAR raw data to prevent the degradation of signal quality caused by data compression. In this manner, a new variation estimation strategy and a new threshold method for block type decision are introduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.12-18
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• 2008

This paper This paper proposes a new inverse-quantization(IQ) table interpolation algorithm, specialized Digital Signal Processor(DSP) instructions and hardware architecture for digital audio codecs. Non-linear inverse quantization algorithm is representatively used in both MPEG-1 Layer-3 and MPEG-2/4 Advanced Audio Coding(AAC). The proposed instructions are optimized for the non-linear inverse quantization. The proposed algorithm can minimize operational complexity which reduces total computational load. Performance comparisons show a significant improvement of average error. The proposed instructions and hardware architecture can reduce 20% of the instruction counts and minimize computational loads of IQ algorithms effectively compared with existing IQ table interpolation algorithms. Proposed algorithm can implement commercial DSPs.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.36-42
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• 1997

This paper presents a new design technique for performing high quality low bit rate image compression. A hybrid coder(HC) which combines Mean Removed Important Coefficient Selection based JPEG(MR-ICS-JPEG) and Adaptive Vector Quantization (AVQ) is proposed. A new quantization table is developed using the Important Coefficient Selection(ICS) method; the importance of each coefficient is determined using the orthonormal property of the DCT. This quantization table is applied to standard JPEG with mean removal(MR) strategy before processing. This scheme, called MR-ICS-JPEG, produces more than 2 dB enhanced performance in terms of PSNR over standard JPEG. A set of homogeneous codebooks is generated by homogeneous training vectors. Before compression, an image is uniformly divided into 8${\times}$8 blocks. Low detail regions such as backgrounds are roughly coded by AVQ while high detail regions such as edges or curves are finely coded by the proposed MR-ICS-JPEG. This hybrid coder procuces consistently about 3 dB improved performance in terms of PSNR over standard JPEG.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.56-63
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• 2009

Nowdays, Most of Internet Contents delivered as a compressed file. It gives many advantages like deduction of communication bandwidth and transmission time etc. In case of JPEG Compression, Quantization is the most important procedure which accomplish the compression. In general signal processing, Quantization is the process which converts continuous analog signal to discrete digital signal. As you known already, Quantization over JPEG compression is to reduce magnitude of pixel value in spatial domain or coefficient in frequency domain. A lot of Data Hiding algorithms also developed to applicable for those compressed files. In this paper, we are going to unveil the influence of quantization table which used in the process of JPEG compression. Even thought most of algorithm modify frequency coefficients with considering image quality, they are ignoring the influence of quantization factor corresponding with the modified frequency coefficient. If existing algorithm adapt this result, they can easily evaluate their performances.