• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2303-2306
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• 2003

The Wavelet Transform providing both of the frequency and time information of an image is proved to be very much effective for the compression of images, and recently lot of studies on coding algorithms for images decomposed by the wavelet transform together with the multiresolution theory are going on. This paper proposes a Quadtree decompositon method of image compression applied to the images decomposed by wavelet transform by using the correlations between pixels .Since the coefficients obtained by the wavelet transform have high correlations between scales, the Quadtree method can reduce the data quantity effectively The experimental image with 256${\times}$256 size was used to compare the Performances of the existing and the proposed compression methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2292-2298
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• 2009

In this paper, the lossy image compression algorithm using the quad tree and the bandlets is proposed. The proposed algorithm transforms input images by the discrete wavelet transform (DWT) and represents the geometrical structures of high frequency bands using the bandlets with a 8 block- size. In addition, the proposed algorithm searches the position information of the significant coefficients by using the quad tree algorithm and computes the magnitude and the sign information of the significant coefficients by using the Embedded Image Coding using Zerotrees of Wavelet Coefficients (EZW) algorithm. The compression result by using the quad tree algorithm improves the PSNR performance of high frequency images up to 1 dB, compared to that of JPEG-2000 algorithm and that of S+P algorithm. The PSNR performance by using DWT and bandlets improves up to 7.5dB, compared to that by using only DWT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.726-736
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• 1997

The textual image compression by pattern matching is a coding scheme that exploits the correlations between patterns. When we compress the Hangul (Korean character) text by patern matching, the collerations between patterns may decrease due to randoem contacts between phonemes. Therefore in this paper we separate connected phonemes to exploit effectively the corrlation between patterns by inducting the amtch. In the process of sequation, we decide whether the patterns have vowel component or not, and then vowels connected with consonant ae separated. When we compare the proposed algorithm with the existing algorith, the compression ratio is increased by 1.3%-3.0% than PMS[5] in lossy mode, by 3.4%-9.1% in lossless mode than that of SPM[7] which is submitted to standard committe for second generation binary compression algorithm.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3185-3199
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• 1997

This paper suggests techniques to reduce coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same com- pression rate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.165-174
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• 2010

The H.264/AVC standard obtained better performance than previous compression standards, but it also increased the computational complexity of CODEC simultaneously. Various techniques recently included at the KTA software developed by VCEG also were increasing its complexity. Especially adaptive interpolation filter has more complexity than two times due to development for coding efficiency. In this paper, we propose low-complexity filter bank to improve speed up of decoding and coding gain. We consists of filter bank of a fixed-simple filter for low-complexity and adaptive interpolation filter for high coding efficiency. Then we compensated using optimal filter at each macroblock-level or frame-level. Experimental results shows a similar coding efficiency compared to existing adaptive interpolation filter and decoding speed of approximately 12% of the entire decoder gained.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.86-95
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• 1998

Intra-frame video compression, which cannot make use of temporal predictions, requires much higher bit rates compared with inter-frame schemes. In order to reduce bit rates, intra-frame predictive coding methods in DCT domain have been studied especially within the framework of the MPEG-4 video coding standard currently being developed. In this paper, we propose novel intra-frame predictive coding methods in DCT domain with the marginal complexity increase over the conventional methods . The proposed methods consist of a DC coefficient prediction method and two AC coefficient prediction methods. The proposed methods consist of a DC coefficient prediction method and two AC coefficient prediction methods. The proposed DC coefficient prediction method makes it possible to adaptively select the prediction directions without overhead bits, by comparing gradients of DC coefficients from neighboring blocks. As the AC coefficient prediction methods, first, we present an effective method which can improve the prediction directions of the MPEG-4 scheme by considering the DC coefficient of the current block to be coded. And, we present another effective method that decision on the prediction is carried out for each AC coefficient. Simulation results show that substantial bit savings can be achieved by the proposed methods.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.125-131
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• 2010

This paper presents the implementation of a EBCOT TIER-1 for JPEG2000 Encoder. JPEG2000 is new standard for the compression of still image for overcome the artifact of JPEG. JPEG2000 standard is based on DWT(Discrete Wavelet Transform) and EBCOT Entropy coding technology. EBCOT(Embedded block coding with optimized truncation) is the most important technology that is compressed the image data in the JPEG2000. However, EBCOT has the artifact because the operations are bit-level processing and occupy the harf of the computation time of JPEG2000 Compression. Therefore, in this paper, we present modified context extraction method for enhance EBCOT computational efficiency and implemented MQ- Coder as arithmetic coder. The proposed system is implemented by Verilog-HDL, under the condition of TSMC 0.25um ASIC library, gate counts are 30,511EA and satisfied the 50MHz operating condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.5
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• pp.822-832
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• 1994

DSC-HDTV image compression algorithm removes spatial, temporal, and amplitude redundancies of an image by using transform coding, motion-compensated predictive coding, and adaptive quantization, respectively. In this paper, leak processing method which is used to recover image quality quickly from scene change and transmission error and adaptive quantization using perceptual weighting factor obtained by HVS are proposed. Perceptual weighting factor is calculated by contrast sensitivity, spatio-temporal masking and frequency sensitivity. Adaptive quantization uses the perceptual weighting factor and global distortion level from buffer history state. Redundant bits according to adaptation of HVS are used for the next image coding. In the case of scene change, DFD using motion compensated predictive coding has high value, large bit rate and unstabilized buffer states since reconstructed image has large quantization noise. Thus, leak factor is set to 0 for scene change frame and leak factor to 15/16 for next frame, and global distortion level is calculated by using standard deviation. Experimental results show that image quality of the proposed method is recovered after several frames and then buffer status is stabilized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.714-725
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• 1997

This paper proposes an image compression method using the wavelet transform and bit-plane coding of wavelet coefficients. The hierarchical application of wavelet transform to an image produces one low resoluation(the subband with lowest frequency) image and several high frequency subbands. In the proposed method, the low resolution image is compressed by a lossless method at 8 bits per each coefficient. However, the high frequency subbands are decomposed into 8 bit planes. With an adptive block coding method, the decomposed bit planes are effectively compressed using localized edge information in each bit plane. In addition, the propsoed method can control bit rates by selectively eliminating lessimportant subbands of low significant bit planes. Experimental results show that the proposed scheme has better performance in the peak signal to noise ratio (PSNR) and compression rate than conventional image coding methods using the wavelet transform and vector quantization.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.