• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.281-285
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• 2000

This paper proposes context-based coding methods for variable length coding of inter-frame DCT coefficients. The proposed methods classify run-level symbols depending on the preceding coefficients. No extra overhead needs to be transmitted, since the information of the previously transmitted coefficients is used for classification. Two entropy coding methods, arithmetic coding and Huffman coding, are used for the proposed context-based coding. For Huffman coding, there is no complexity increase from the current standards by using the existing inter/intra VLC tables. Experimental results show that the proposed methods give ～ 19% bits gain and ～ 0.8 dB PSNR improvement for adaptive inter/intra VLC table selection, and ～ 37% bits gain and ～ 2.7dB PSNR improvement for arithmetic coding over the current standards, MPEG-4 and H.263. Also, the proposed methods obtain larger gain for small quantizaton parameters and the sequences with fast and complex motion. Therefore, for high quality video coding, the proposed methods have more advantage.

• Journal of IKEEE
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• v.21 no.3
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• pp.280-283
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• 2017

HEVC (high efficiency video coding) exploits CABAC (context-based adaptive binary arithmetic coding) for entropy coding, where a context model estimates the probability for each syntax element. In this paper, a context modeler was designed and implemented for CABAC decoding. lookup table was used to reduce computation and to increase speed. 12 simulations for HEVC standard test sequences and encoder configurations were performed, and the context modeler was verified to perform correction operations. The designed context modeler was synthesized in 0.18um technology. Maximum frequency, maximum throughput, and gate count are 200 MHz, 200 Mbin/s, and 29,268 gates, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.183-189
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• 2013

This paper proposes a novel lossless image compression scheme composed of direction-adaptive prediction and context-based entropy coding. In the prediction stage, we analyze the directional property with respect to the current coding pixel and select an appropriate prediction pixel. In order to further reduce the prediction error, we propose a prediction error compensation technique based on the context model defined by the activities and directional properties of neighboring pixels. The proposed scheme applies a context-based Golomb-Rice coding as the entropy coding since the coding efficiency can be improved by using the conditional entropy from the viewpoint of the information theory. Experimental results indicate that the proposed lossless image compression scheme outperforms the low complexity and high efficient JPEG-LS in terms of the coding efficiency by 1.3% on average for various test images, specifically for the images with a remarkable direction the proposed scheme shows better results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.355-360
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• 2011

In this paper, we propose a new context-based adaptive binary arithmetic coding (CABAC) method for lossless image compression. Since the conventional CABAC in H.264/AVC was originally designed for lossy coding, it does not yield adequate performance during lossless coding. Therefore, we proposed an improved CABAC method for lossless intra coding by considering the statistical characteristics of residual data in lossless intra coding. Experimental results showed that the proposed method reduced the bit rate by 18.2%, compared to the conventional CABAC for lossless intra coding.

• The Journal of the Acoustical Society of Korea
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• v.31 no.7
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• pp.435-440
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• 2012

We propose optimal contexts for context-adaptive coding of stereo parameters in parametric stereo (PS) of enhanced aacPlus. For the quantized indexes of stereo parameters, 8 context candidates were proposed based on the index values and their combinations adjacent to a source index in the time-stereo band domain, where the time-stereo band region was further divided into 4 regions based on refresh/non-refresh frames and stereo bands. The optimal contexts for each region were proposed by experiments, which are expected to be used for context-adaptive coding of PS for improved performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.425-428
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• 2005

This paper is made up Advanced Image Coding(AIC) that combines algorithms from next generation image coding standard, H.264/MPEG-4 Part 10 advanced video coding(AVC) and still image compression standard, JPEG(Joint Photographic Experts Group). AIC combines intra frame block prediction from H.264 with a JPEG style discrete cosine transform and quantization, followed by Context-based Adaptive Binary Arithmetic Coding(CABAC) as used in H.264. In this paper, we analyzes the efficiency of the AIC algorithm and JPEG and JPEG-2000, and it presents of result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.97-100
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• 2007

In order to overcome many defects in the current JPEG standard of still image compression, the new JPEG2000 standard has been development. The JPEG2000 standard is based on the principles of DWT and EBCOT Entropy Coding. EBCOT(Embedded block coding with optimized truncation) is the most important technology in the latest image-coding standard, JPEG2000. However, EBCOT occupies the highest computation time to operate bit-level processing. Therefore, many researches have achieved methods to minimize computation speed of EBCOT. Thus, this paper proposes the method of context-extraction that improves computational architecture. This paper proposes efficient context coding method. The proposed algorithm would apply to hard-wired JPEG2000 Encoder that is used for compression of high resolution image.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.332-340
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• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.495-502
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• 2018

JVET (Joint Video Exploration Team) which explored evolving technologies of video coding with capabilities beyond HEVC (High Efficiency Video Coding), released a references software codec named the Joint Exploration Model (JEM) for performance verification of coding technologies. JEM has 67 intra prediction modes that extend the 35 modes of HEVC for intra prediction. Therefore, the enhancement of the coding performance is limited due to the overhead of prediction mode coding. In this paper, we analyze the probabilities of prediction modes selections, and then we propose a more efficient intra prediction mode coding based on the results of analyzed mode occurrence. In addition, we propose a context modeling for CABAC (Context-Adaptive Binary Arithmetic Coding) of the proposed mode coding. Experimental results show that the BD-rate gain is 0.02% on the AI (All Intra) coding structure compared to JEM 7.0. We need to optimize context modeling for additional coding performance enhancement.