With a direct impact on compression performance, optimal quantization scheme is crucial for transformbased ECG data compression. However, traditional optimization schemes derived with signal adaption are commonly inherent with signal dependency and unsuitable for real-time application. In this paper, the variety of arrhythmia ECG signal is utilized for optimizing the quantization scheme of wave...

Vector Quantization (VQ) is one of the lossy image compression techniques. VQ comprises of three different phases: Codebook Generation, Image Encoding and Image Decoding. The performance of VQ is mainly based on the codebook generation phase. In this paper, five different codebook generation techniques namely the Simple Codebook Generation (SCG), Ordered Codebook Generation (OCG), Codebook Gene...

In this paper, an unsupervised approach based on Evolving Vector Quantization (EVQ) is presented for enhancing dermatology images for skin lesion segmentation. Vector Quantization (VQ) as a famous compression technique has been widely used in image signal compression and speech signal compression. The EVQ algorithm extends the Linde, Buzo, and Gray (LBG) Vector Quantization method with Particle...

Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary ...

In this paper, a non-blind watermarking technique has been proposed where a grayscale watermark is embedded. In the embedding process, first the host image is decomposed by wavelet transform and a middle frequency subband is selected where the watermark is embedded. Then, both the watermark and the selected subband are encoded by BTC-PF method. For each block, BTC-PF method returns quantization...

In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. The nature of this problem is such that the data sequences are never repeated, but they are rather in chaotic region. However, these data sequences are correlated between past, present, and future data in high order. We use Cascade Error Projection (CEP) learning algorithm to c...

Video compression uses encoding to reduce the number of bits that are used for representing a video file in order to store and transmit it at a smaller size. A decoder reconstructs the received data into a representation of the original video. Video coding standards determines how the video compression should be conducted and one of the latest standards is High Efficiency Video Coding (HEVC). O...

Rendering geometrically detailed 3D models requires the transfer and processing of large amounts of triangle and vertex geometry data. Compressing the geometry bitstream can reduce bandwidth requirements and alleviate transmission bottlenecks. In this paper, we show vector quantization to be an effective compression technique for triangle mesh vertex data. We present predictive vector quantizat...

Interference alignment (IA) is a promising technique that allows high capacity gains in interfering channels. In this paper we consider the design of iterative minimum mean squared error (MMSE)-based IA techniques for the downlink of broadband wireless systems with limited feedback and employing orthogonal frequency division multiplexing (OFDM) signals. A quantized version of the channel state ...

In this work we introduce an efficient method to limit the impact of the hiding process on the quality of the cover speech. Vector quantization of the speech spectral information reduces drastically the number of the secret speech parameters to be embedded in the cover signal. Compared to scalar hiding, vector quantization hiding technique provides a stego signal that is indistinguishable from ...