An Image Sequence Coder Based on Vector Quantization and Progressive Transmission

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

A hybrid compression method for the NMR data based on window averaging and Discrete Cosine Transform

In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

Multimedia data is a popular communication medium, but requires substantial storage space and network bandwidth. Vector quantization (VQ) is suitable for multimedia data applications because of its simple architecture, fast decoding ability, and high compression rate. Full‐search VQ can typically be used to determine optimal codewords, but requires considerable computational time and resources. In this study, a hybrid VQ combining a tree structure and a Voronoi diagram is proposed to improve VQ efficiency. To efficiently reduce the search space, a tree structure integrated with principal component analysis is proposed, to rapidly determine an initial codeword in low‐dimensional space. To increase accuracy, a Voronoi diagram is applied to precisely enlarge the search space by modeling relations between each codeword. This enables an optimal codeword to be efficiently identified by rippling an optimal neighbor from parts of neighboring Voronoi regions. The experimental results demonstrated that the proposed approach improved VQ performance, outperforming other approaches. The proposed approach also satisfies the requirements of handheld device application, namely, the use of limited memory and network bandwidth, when a suitable number of dimensions in principal component analysis is selected.

With the rapid development of the mobile communication technology, the number of long-term evolution (LTE) users and the volume of data is continually increasing, which greatly increases the amount of data transferred by fronthaul, resulting in a huge investment in optical fiber resource. In order to control the consumption of optical fiber resource, lower the cost of operator and avoid congestion in the premise of increasing data volume, it is necessary to compress the data for fronthaul. According to the characteristics of the LTE baseband signal, this paper proposes a data compression scheme on the basis of discrete cosine transform (DCT) and vector quantization. Firstly, the time domain signal is transformed in DCT. And then, the k-means clustering algorithm is used for vector quantization on the transformed signal. Finally, the Huffman coding is applied to further increase the compression ratio (CR) under the promise of an acceptable error. The simulation results show that the scheme proposed has a good performance in both compression ratio and error vector Magnitude (EVM) for the LTE baseband signal base on orthogonal frequency division multiplexing (OFDM).

In this paper we study coding of video sequences at very low bitrates using an entropy-constrained variable block size residual vector quantizer. For a given complexity, it is well understood that structured vector quantizers perform better than unstructured and unconstrained vector quantizers. A combination of structured vector quantizers is used in this paper to encode the video sequences. The proposed codec uses a Discrete Cosine Transform (DCT) based transform vector quantizer and a variable-rate residual vector quantizer in cascade. The transform-VQ captures the low-frequency information using only a small portion of the bit-budget, while the later stage residual VQ captures the high-frequency information using the remaining bits. A strategy to adaptively refine only areas of high activity using recursive decomposition and selective refinement is used in the later stages. An entropy constraint is used to modify the codebooks to allow better entropy coding of the indices. Performance of the proposed codec is evaluated and compared with the H.263 based codec. Experimental results show not only better quantitative performance but also significantly better perceptual quality.

A classification algorithm in the discrete cosine transform (DCT) domain for the classified vector quantization (CVQ) technique is proposed. The classifier employs four DCT coefficients of 4*4 subblock as edge-oriented features. The classifier is designed using a cluster-seeking algorithm to ensure that the centroid of a set of vectors in a class always belong to that class. Since the classification is performed in the DCT domain, this approach can be easily extended to the DCT transform coding technique. Simulation results show that a good visual quality of the coded image at fixed rates in the 0.625-0.813 b/pixel (bpp) range is obtained with comparable complexity. The weighted MSE (WMSE) analysis in conjunction with the proposed classifier is discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, a new block-transform-based image compression scheme is proposed by combining vector quantization (VQ) and two transformations, discrete cosine transform (DCT) and vector-embedded Karhunen-Loève transform (VEKLT). First, 8×8 blocks from an input image are normalized and vector-quantized. Then, the difference between the original block and its vector-quantized block is transformed by VEKLT. In parallel, the original block is transformed by DCT. All blocks are classified into two categories (DCT and VEKLT) to minimize arithmetic code length. After that, quad tree decomposition is performed on the binary index image which indicates where a block belongs to one of the two categories. Experimental results show that the proposed scheme outperforms JPEG in peak signal-to-noise ratio (PSNR) and visual quality at high detail.

Presents two novel algorithms for compression of multilead electrocardiograms (MLECGs). In the first, the MLECG was decorrelated using a discrete cosine transform (DCT) and then a discrete wavelet transform (DWT) was applied on each channel after the removal of the least significant channels in the DCT domain. Significant compression ratios (CR) were achieved by retaining only the consequential DWT coefficients. The full potential of DWT was further exploited by appropriate quantization of these coefficients. Near perfect reconstruction was obtained for a CR of 1:8. This approach is highly suited for real time applications including that of high frequency Holter ECG. In the other algorithm, first level of compression was achieved by truncating the DWT while storing the highly significant coefficients from the truncated portion. Transform domain pole-zero modeling (TDPM) was then applied on the DWT compressed ECG to achieve higher compression ratios. In TDPM the DCT of a cycle of ECG is modeled as the impulse response of a pole-zero filter using Smith's (1983) method. Further compression was achieved by the application of vector quantization (VQ) on the model parameters. This algorithm resulted in a CR of 1:80 without significant distortion. The accuracy of reconstruction was checked using both qualitative and quantitative measures. The proposed algorithms were validated on both standard and local databases.

Block-based transform coding employing the discrete cosine transform (DCT) is a popular technique in image and video compression. We consider Wiener-filter-based restoration for transform-coded images and motion video. The scheme operates at the decoder end. It capitalizes on the residual correlation among quantized DCT coefficients and the quantization errors. The scheme, termed error pattern compensation or EPC in short, is derived by simplifying and extending related work of other researchers. When applied to motion video, it is activated for intraframe-coded macroblocks only. It first classifies an encoded image block according to some visually meaningful features and vector quantization. Different Wiener filters can be designed for different classes of input blocks. Experimental results show that the scheme yields performance improvement in the range of a few percent in MSE or PSNR. It is more effective in restoring image blocks with greater quantizing distortion. Results also show that intraframe-coded pictures usually benefit the most from EPC, followed by predictive-coded pictures and then by bidirectional-predictive-coded pictures. At times, the last two types of pictures show performance degradation rather than gain. Possible reasons are discussed.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Currently, much work has been done in the area of image storage and retrieval. However, the overall performance has been far from practical. A highly integrated wavelet-based image management system is proposed in this paper. By integrating wavelet-based solutions for image compression and decompression, content-based retrieval and progressive transmission, much higher performance can be achieved. The multiresolution nature of the wavelet transform has been proven to be a powerful tool to represent images. The wavelet transform decomposes the image into a set of subimages with different resolutions. From here three solutions for key aspects of image management are reached. The content-based image retrieval (CBIR) features of our system include the color, contour, texture, sample, keyword and topic information of images. The first four features can be naturally extracted from the wavelet transform coefficients. By scoring the similarity of users' requests with images in the database, those who have higher scores are noted and the user receives feedback. Image compression and decompression. Assuming that details at high resolution and diagonal directions are less visible to the human eye, a good compression ratio can be achieved. In each subimage, the wavelet coefficients are vector quantized (VQ), using the LGB algorithm, which is improved in our approach to accelerate the process. Higher compression ratio can be achieved with DPCM and entropy coding method applied together. With YIQ representation, color images can also be effectively compressed. There is a very low load on the network bandwidth by transmitting compressed image data across the network. Progressive transmission is possible by employment of the multiresolution nature of the wavelet, which makes the system respond faster and the user-interface more friendly. The system shows a high overall performance by exploring the excellent features of wavelet, and integrating key aspects of image management. An image retrieval service using Java is also available on the world wide web (www), to demonstrate the system's features.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Interactions between the gut bacterial community of Exopalaemon carinicauda and infection by Enterocytozoon hepatopenaei

Vector quantization (VQ) is a signal compression technique which can provide high compression rates, and the self-organizing map (SOM) can be employed in the generation of VQ codebooks. Exploiting the ordering property of SOM, the encoding process can be considerably accelerated by using a two-level search. In this paper, we deal with the VQ of prediction error (residual) images in image sequence coding. The results show that the codebooks generated by SOM and the widely-used LBG algorithm achieve almost the same performance, but the encoding process can be realized in a more efficient way by exploiting the ordering property of SOM.

OFDM has been adopted as a standard for various high data rate wireless communication systems due to several benefits. One of the major drawbacks in OFDM system is its high Peak-to-Average Power Ratio (PAPR) of its transmitted signals thereby making transmitter amplifier to operate beyond its linear range of operation resulting in signal distortion. In this research work a simple method for reducing Peak-to-Average Power Ratio in OFDM is presented. The method is based on Discrete Cosine Transform (DCT) aided successive addition and subtraction (SAS) of data symbols within a single OFDM frame. Performance of the proposed method is evaluated and compared with the existing techniques. Joint reduction in PAPR by combining SAS preprocessed DCT technique and companding is also presented where system utilizes companding technique to further reduce the PAPR of the OFDM signal. Keywords: OFDM; PAPR; DCT; SAS; CompandingOFDM has been adopted as a standard for various high data rate wireless communication systems due to several benefits. One of the major drawbacks in OFDM system is its high Peak-to-Average Power Ratio (PAPR) of its transmitted signals thereby making transmitter amplifier to operate beyond its linear range of operation resulting in signal distortion. In this research work a simple method for reducing Peak-to-Average Power Ratio in OFDM is presented. The method is based on Discrete Cosine Transform (DCT) aided successive addition and subtraction (SAS) of data symbols within a single OFDM frame. Performance of the proposed method is evaluated and compared with the existing techniques. Joint reduction in PAPR by combining SAS preprocessed DCT technique and companding is also presented where system utilizes companding technique to further reduce the PAPR of the OFDM signal. Keywords: OFDM; PAPR; DCT; SAS; Companding

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of vector quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and human visual system (HVS) models. The error model assumed is the Laplacian distribution with mean, (lambda) , computed from a sample of the input image. A Laplacian distribution with mean, (lambda) , is generated with a uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produced the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, (lambda) , that is included in the coded file to repeat the codebook generation process for decoding.