Jar-Ferr Yang

Source number estimators using transformed Gerschgorin radii

We introduce effective uses of Gerschgorin radii of the unitary transformed covariance matrix for source number estimation. There are two approaches, likelihood and heuristic, used for developing the detection criteria. The likelihood approach combines the Gerschgorin radii to the well-known source number detectors and improves their detection performances for Gaussian and white noise processes. It is verified that the Gerschgorin likelihood estimators (GLE) are consistent. The Gerschgorin AIC yields a consistent estimate and the Gerschgorin MDL criterion does not tend to underestimate for small or moderate data samples. The heuristic approach applying the Gerschgorin disk estimator (GDE) developed from the projection concept, overcomes the problem in cases of small data samples, an unknown noise model, and data dependency. Furthermore, the detection performances of both approaches through the suggested rotations and averaging can be further improved. Finally, the proposed and existing criteria are evaluated in various conditions by using simulated and measured experimental data. >

A Fast Mode Decision Algorithm and Its VLSI Design for H.264/AVC Intra-Prediction

In this paper, we present a fast mode decision algorithm and design its VLSI architecture for H.264 intra-prediction. A regular spatial domain filtering technique is proposed to compute the dominant edge strength (DES) to reduce the possible predictive modes. Experimental results revealed that the proposed fast intra-algorithm reduces 40% computation with slight peak signal-to-noise ratio (PSNR) degradation. The designed DES VLSI engine comprises a zigzag converter, a DES finite-state machine (FSM), and a DES core. The former two units handle memory allocation and control flow while the last performs pseudoblock computation, edge filtering, and dominant edge strength extraction. With semicustom design fabricated by 0.18 mum CMOS single-poly-six-metal technology, the realized die size is roughly 0.15 times 0.15 mm2 and can be operated at 66 MHz.

Combined techniques of singular value decomposition and vector quantization for image coding

The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity.

Enhanced Intra-4

In this paper, we propose an enhanced rate-distortion cost function, which combines the sum of absolute integer-transformed differences (SAITD) and a rate predictor for H.264/AVC intra-4times4 mode decision. To reduce the computation of the SAITD, we further develop a fast computation algorithm, which successfully uses the property of linear transform and the fixed spatial relationship of predicted pixels in intra-modes. Simulation results show that the enhanced cost function with less computation achieves better coding performance than the cost function suggested in H.264 reference software JM 6.1d

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In this paper, computation reduction algorithms of motion estimation are developed for low rate video coders. By jointly considering motion estimation, discrete cosine transform, and quantization, the all-zero and zero-motion detection algorithms are suggested to reduce the computation of motion estimation. Simulation results show that many unnecessary computations in motion estimation are greatly reduced. For the Akiyo sequence, the proposed algorithms reduce the average numbers of search points from 28.90 and 4.99 to 2.89 and 1.40 for three-step search and diamond-search algorithms, respectively.

4 Mode Decision for H.264/AVC Coders

In H.264/AVC intra-frame coding, the rate-distortion optimization (RDO) is employed to select the optimal coding mode to achieve the minimum rate-distortion cost. Due to a large number of combinations of coding modes, the computational burden becomes extremely high in intra-prediction. In this paper, we propose two fast, efficient but reliable direction detection algorithms by computing subblock and pixel direction differences. Both proposed methods effectively estimate the edge direction inside the block to narrow down the predictive modes to reduce the RDO computation. Experimental results show that the proposed methods can reduce the encoding time by about 60% with negligible loss of coding performance. For hardware realization, a fast mode decision VLSI circuit for intra-prediction with the silicon core size of 0.12 times 0.12 mm2 at 0.18-mum CMOS technology is implemented. The fast mode decision VLSI in three-stage pipelined architecture operated at 173 MHz can encode 30 fps real-time videos up to Level 5.

Computation reduction for motion search in low rate video coders

In video coders, the optimal coding mode decision for each coding block can be achieved by exhaustively calculating the rate-distortion cost, which simultaneously considers the distortion performance and the coding bit consumption of all possible modes. The best mode is chosen from the one with the minimum Lagrange cost. To avoid the expensive computation of Lagrange costs, in this paper, we propose transform-domain bit-rate estimation and distortion measures, based on quantized and inverse quantized integer transform coefficients, for the inter-mode decision in H.264/AVC coders. With the proposed scheme, entropy coding, inverse transform, and pixel-reconstructions are not required in the process. With ignorable degradation in coding performance, simulations demonstrate that the proposed estimation method achieves about 40% reduced computation time of rate-distortion cost for the inter-mode decision and saves about 17% total encoding time while combining with fast motion estimation and fast mode decision algorithms.

Effective Subblock-Based and Pixel-Based Fast Direction Detections for H.264 Intra Prediction

A merging procedure for variable-size block motion estimation is proposed to reduce the computation for block-size decision. A smaller block-size is initially used for motion estimation. An adaptive threshold, which depends on the information obtained from the motion estimation, quantization parameter, and rate distortion cost function is used to determine if the motion vectors of the neighboring blocks should be merged or not. We simulate the proposed merging procedure using an H.264 video encoder to determine if 16x16, 16x8, 8x16, or 8x8 blocksize should be used for each macroblock. Based on the simulations, performance of the proposed method is close to that of H.264 JM2.1 when 16x16, 16x8, 8x16 and 8x8 block modes are enabled and the exhaustive search method is used to determine the block-sizes. Computational complexity can be significantly reduced by the proposed algorithm since it only performs the motion search for one block-size.

Efficient rate-distortion estimation for H.264/AVC coders

In this work, high throughput hardware architectures for fast computation of the 2-D forward, inverse and Hadamard transforms suggested in H.264 advanced video coders (AVC) are presented. After complexity and efficiency analyses, we find that the proposed architectures could provide higher throughput rate and realize in a smaller chip area than the conventional row-column approaches. The proposed architectures are synthesized with TSMC 0.35 /spl mu/m technology. The synthesized multiple transform architecture could process 800 M samples/sec at 100 MHz for all the three transforms.

Fast variable-size block motion estimation using merging procedure with an adaptive threshold

In this paper, we propose two eigen-based fuzzy C-means (FCM) clustering algorithms to accurately segment the desired images, which have the same color as the pre-selected pixels. From the selected color pixels, we can first divide the color space into principal and residual eigenspaces. Combined eigenspace transform and the FCM method, we can effectively achieve color image segmentation. The separate eigenspace FCM (SEFCM) algorithm independently applies the FCM method to principal and residual projections to obtain two intermediate segmented images and combines them by logically selecting their common pixels. Jointly considering principal and residual eigenspace projections, we then suggest the coupled eigen-based FCM (CEFCM) algorithm by using an eigen-based membership function in clustering procedure. Simulations show that the proposed SEFCM and CEFCM algorithms can successfully segment the desired color image with substantial accuracy.