Yung-Gi Wu

An adaptive 3-D discrete cosine transform coder for medical image compression

A new three-dimensional (3-D) discrete cosine transform (DCT) coder for medical images is presented. In the proposed method, a segmentation technique based on the local energy magnitude is used to segment subblocks of the image into different energy levels. Then, those subblocks with the same energy level are gathered to form a 3-D cuboid. Finally, 3-D DCT is employed to compress the 3-D cuboid individually. Simulation results show that the reconstructed images achieve a bit rate lower than 0.25 bit per pixel even when the compression ratios are higher than 35. As compared with the results by JPEG and other strategies, it is found that the proposed method achieves better qualities of decoded images.

Medical image compression by sampling DCT coefficients

Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in spatial domain will be transformed into spectral domain after the transformation to attain more compression gains. Based on the quantization strategy, coefficients of low amplitude in the transformed domain are discarded and significant coefficients are preserved to increase the compression ratio without inducing salient distortion. In this paper, we use an adaptive sampling algorithm by calculating the difference area between correct points and predicted points to decide the significant coefficients. Recording or transmitting the significant coefficients instead of the whole coefficients achieves the goal of compression. On the decoder side, a linear equation is employed to reconstruct the coefficients between two sequent significant coefficients. Simulations are carried out to different medical images, which include sonogram, angiogram, computed tomography, and X-ray images. Consequent images demonstrate the performance at compression ratios of 20-45 without perceptible alterations. In addition, two doctors are invited to verify that the decoded quality is acceptable for practical diagnosis. Therefore, our proposed method is found to preserve information fidelity while reducing the amount of data.

Medical image compression by discrete cosine transform spectral similarity strategy

Due to bandwidth and storage limitations, medical images must be compressed before transmission and storage. However, the compression reduces the image fidelity, especially when the images are compressed at low bit rates. The reconstructed images suffer from blocking artifacts and the image quality is severely degraded under high compression ratios. In this paper, we present a strategy to increase the compression ratio with low computational burden and excellent decoded quality. We regard the discrete cosine transform as a bandpass filter to decompose a sub-block into equal-sized bands. After a band-gathering operation, a high similarity property among the bands is found. By utilizing the similarity property, the bit rate of compression can be greatly reduced. Meanwhile, the characteristics of the original image are not sacrificed. Thus, it can avoid the misdiagnosis of diseases. Simulations were carried out on different kinds of medical images to demonstrate that the proposed method achieves better performance when compared to other existing transform coding schemes, such as JPEG, in terms of bit rate and quality. For the case of angiogram images, the peak signal-to-noise-ratio gain is 13.5 dB at the same bit rate of 0.15 bits per pixel when compared to the JPEG compression. As for the other kinds of medical images, their benefits are not so obvious as for angiogram images; however, the gains for them are still 4-8 dB at high compression ratios. Two doctors were invited to verify the decoded image quality; the diagnoses of all the test images were correct when the compression ratios were below 20.

An efficient BTC image compression technique

This paper presents a moment preserving and visual information dominance technique to achieve the low-bit rate block truncation coding (BTC). Compared with other existing strategies as transform coding and vector quantization, conventional BTC compression has the advantage of simple and fast computation. Nevertheless the compression ratio is limited by its low efficiency. Our proposed technique accomplishes the goal of simple computation with variable bit rate selection by the moment preservation and information extraction algorithm. The proposed technique has the advantage of simple operations and it does not require complicated mathematical computations. Thus, the overall computation does not increase the burden compared with ordinary BTC. The simulations are carried with natural images to evaluate the performance. The generated decoded images have moderate quality with a bit rate of 0.5-1.0 bit/pixel.

Low bit rate subband DCT image compression

This paper describes two novel methods to improve the efficiency of subband coding by means of the energy compactness in the frequency domain. Our coding scheme applies a variable block size DCT to the subband signal to get a high coding efficiency. A class driven segmentation is devised as a variable block partition scheme. Besides, an information oriented coding strategy without side information is developed. The strategy not only retains the image properties but also reduces the computation overhead. Simulations are carried out to the Lena image, and their results show the effectiveness of the two approaches especially for low bit rate applications.

Design of a fast vector quantization image encoder

Vector quantization (VQ) is an efficient technique for signal compression. However, it requires much encoding time to find the closest codeword for every input vector. We propose a fast encoding method to speed up the encoding. With the help of a table that is created off-line and can be used by all the images, the encoder searches only part of the entire codebook. The proposed method is implemented to encode Lena and other images to test its performance. Compared to full-searching VQ (FS-VQ), although the encoder searches only about 20 codewords in the codebook for every input vector, more than 95% of the codewords searched by the proposed method are the same as the results searched by FS-VQ on average. In addition, we also adopt partial distortion searching (PDS) and lookup table (LUT) to decrease the mathematic computation. This saves 98.44% of the encoding time and 98.07% of the mathematic operation while encoding Lena. The proposed method is superior to all the existing fast VQ encoding methods. While encoding 100 nature images for testing, it can save more than 97% of the encoding time and mathematic operations, but the PSNR decays at most only 0.19 dB, which is invisible to human eyes.

Markov system for image vector quantization coding

Vector quantization (VQ) has been accepted as one of the most effective image compression methods with provable rate-distortion optimality. The outputs of VQ are a collection of indices, which corre- spond to the addresses of the codevectors in the codebook. The indices are, however, not mutually independent. They are in fact very highly correlated and are thus appropriately described by a Markov system. In this paper, a Markov system for VQ indices is introduced. Statistics are gathered for various scans, such as the zig-zag, Peano, row-major and column-major scans. The proposed method, like address VQ, achieves the same image quality as conventional VQ. Simulation results show that the proposed method achieves a better bit-rate reduction than Address- VQ. Besides, both the computational complexity and memory needed for the proposed method are lower. Nevertheless, the only extra operation needed by the proposed method is a simple table retrieval operation on both the encoder side and the decoder side. We believe that it is a method worth further exploration.

Differential pulse code modulation predictor design procedure using a genetic algorithm

Digitized images are used widely in current industrial applications; however, digital images cost a huge storage capacity, leading to transmission bandwidth constraints. Therefore, an image compression technique was developed to solve this drawback. Differential pulse code modulation (DPCM) is the most widely used method among predictive image coding schemes. It is based on the notation of quantizing a prediction error signal after the prediction operation to reduce the original data complexity. The design of the predictor influences the performance of the whole DPCM. Therefore, the predictor constitutes a central topic in the DPCM system. We make efforts toward the design of a predictor by using a genetic algorithm (GA). By the operations of crossover and mutation, several predictor coefficients are yielded after hundreds of generations. The predictor generated by the GA outperforms the current eight different predictive schemes in Joint Photographic Experts Group lossless (JPEG-LS) still image compression standard. Because the JPEG-LS compression scheme is currently the most widely used lossless compression method, our results contribute to the applications of compression.

Fast and low bit rate fractal image encoding

Fractal theory has been widely applied in the field of image compression due to its advantages of resolution independence, fast decoding, and high compression ratio. However, it has a fatal shortcoming of intolerant encoding time for every range block to find its corresponding best matched domain block. In this work, an algorithm is proposed to improve this time-consuming encoding drawback by an adaptive searching window, partial distortion elimination (PDE), and characteristic exclusion algorithms. The proposed methods efficiently decrease the encoding time. In addition, the compression ratio is also raised due to the reduced searching window. While conventional full search fractal encoding to compress a 512×512 image needs to search 247,009 domain blocks for every range block, our experimental results show that our proposed method only needs to search 122 domain blocks, which is only 0.04939% compared to a conventional fractal encoder for every range block to encode a Lena 512×512 8-bit gray image at a bit rate of 0.2706 bits per pixel (bpp) while maintaining almost the same decoded quality in visual evaluation. In addition, the visual decoded quality of the proposed method is better than the most widely used JPEG compressor.

VQ bit rate reduction technique by transform compression

A technique to reduce the overhead of vector quantization (VQ) is developed. This method exploits the high correlation property among neighboring blocks. By rearranging the codebook after the training process, the indices of neighboring vectors have fewer varieties, then, the gains of compression is attained. For the energy compactness advantage of the discrete cosine transform (DCT), the indices buffer formed after the encoding phase are fed to the DCT to reduce the bit rate further. Statistics illustrators are addressed to prove the efficient performance of the proposed method. Experiments are carried out on natural gray images. Simulation results show that our method decreases the bit rate more than 50% with the same reconstructed image quality compared to standard VQ coding.