We construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.

The advent of GPUs with programmable shaders on handheld devices has motivated embedded application developers to utilize GPU to offload computationally intensive tasks and relieve the burden from embedded CPU. In this work, we propose an image processing toolkit on handheld GPU with programmable shaders using OpenGL ES 2.0 API. By using the image processing toolkit, we show that a range of image processing algorithms map readily to handheld GPU. We employ real-time video scaling, cartoon-style non-photorealistic rendering, and Harris corner detector as our example applications. In addition, we propose techniques to achieve increased performance with optimized shader design and efficient sharing of GPU workload between vertex and fragment shaders. Performance is evaluated in terms of frames per second at varying video stream resolution.

In this paper, we explore the key factors in the design and implementation of visual computing (image processing and computer vision) algorithms on the massive parallel GPU (graphics processing units). The goal of the exploration is to provide common perspective and guidelines of using GPU for visual computing applications. We have selected three nontrivial applications (multiview stereo matching, linear feature extraction, and JPEG2000 image encoding) for the benchmarks, which show different characteristics in GPU parallel computing. Intensive analysis is performed to evaluate the characteristic of each algorithm and its effect on the performance. Based on this, we draw general guidelines of using GPU for the visual computing algorithms.

In this paper a novel and computationally inexpensive analytic mean square error (MSE) distortion rate (D-R) estimator for SPIHT which generates a nearly exact distortion rate (D-R) function for the 2D and 3D SPIHT algorithm is presented. The analytical formula is derived from the observations that for any bit-plane coder, the slope of the D-R curve is constant for each level of the bit plane. Furthermore the slope of D-R curve reduces by a factor proportional to the level of the bit plane. An application of the derived results is presented in the area of 2D SPIHT transmission employing a binary symmetric channel (BSC) and Reed Solomon (RS) forward error correction (FEC) codes. Utilizing our D-R estimate, we employ unequal error protection (UEP) and equal error protection (EEP) in order to minimize the end to end mean square error (MSE) distortion of the transform domain. UEP yields a significant performance gain relative to EEP only when the average number of parity bits for a group of packets is constrained. When both the source rate and channel code rate varied under a bit budget constraint, optimal UEP yields only a slight improvement over the optimal EEP. A major contribution of this paper is the simple and extremely accurate analytical D-R model which potentially improves upon pre-existing methodologies and applications that rely on an accurate and computationally inexpensive D-R estimate. Another important contribution is that the optimum EEP, which requires almost no header information and can easily be computed using our method, is only slightly worse than the optimum UEP.

In this paper we consider the problem of robust image coding and packetization for the purpose of communications over slow fading frequency selective channels and channels with a shaped spectrum like those of digital subscribe lines (DSL). Towards this end, a novel and analytically based joint source channel coding (JSCC) algorithm to assign unequal error protection is presented. Under a block budget constraint, the image bitstream is de-multiplexed into two classes with different error responses. The algorithm assigns unequal error protection (UEP) in a way to minimize the expected mean square error (MSE) at the receiver while minimizing the probability of catastrophic failure. In order to minimize the expected mean square error at the receiver, the algorithm assigns unequal protection to the value bit class (VBC) stream. In order to minimizes the probability of catastrophic error which is a characteristic of progressive image coders, the algorithm assigns more protection to the location bit class (LBC) stream than the VBC stream. Besides having the advantage of being analytical and also numerically solvable, the algorithm is based on a new formula developed to estimate the distortion rate (D-R) curve for the VBC portion of SPIHT. The major advantage of our technique is that the worst case instantaneous minimum peak signal to noise ratio (PSNR) does not differ greatly from the averge MSE while this is not the case for the optimal single stream (UEP) system. Although both average PSNR of our method and the optimal single stream UEP are about the same, our scheme does not suffer erratic behavior because we have made the probability of catastrophic error arbitarily small. The coded image is sent via orthogonal frequency division multiplexing (OFDM) which is a known and increasing popular modulation scheme to combat ISI (Inter Symbol Interference) and impulsive noise. Using dual adaptive energy OFDM, we use the minimum energy necessary to send each bit stream at a particular probability of bit error. An added benefit of OFDM over serial transmission schemes is that some degree of progressiveness of SPIHT is preserved by transmitting both the VBC and LBC streams in parallel.

In this letter, a novel and computationally inexpensive analytic mean square error (mse) distortion rate (D-R) estimator for SPIHT which generates a nearly exact D-R function for the two- and three-dimensional SPIHT algorithm is presented. Utilizing our D-R estimate, we employ unequal error protection and equal error protection in order to minimize the end to end MSE distortion of the transform domain. A major contribution of this letter is the simple and extremely accurate analytical D-R model which potentially improves upon pre-existing methodologies and applications that rely on an accurate and computationally inexpensive D-R estimate.

The error resilient and error concealment 3-D SPIHT (ERC-SPIHT) are introduced, which are encoded with an asymmetric tree structure. Next, error resilient video transmission is presented using the ERC-SPHITC algorithm with rate-compatible punctured convolutional (RCPC) code and Reed-Solomon code.

Summary form only given. Hyperspectral images were generated through the collection of hundreds of narrow and contiguously spaced spectral bands of data producing a highly correlated long sequence of images. An investigation and comparison was made on the performance of several three-dimensional embedded wavelet algorithms for compression of hyperspectral images. These algorithms include 3D-SPIHT, AT-3DSPIHT, 3D-SPECK (three-dimensional set partitioned embedded block), and JPEG2000. 3D-SPIHT is the three-dimensional extension of the state-of-the-art SPIHT (set partitioning in hierarchical trees) image compression algorithm. AT-SPIHT uses the same coding algorithms as 3D-SPIHT, but utilizes a more efficient, asymmetric tree structure. 3D-SPECK is a version of two-dimensional SPECK modified to encode 3D subband blocks. For an image sequence, 3D-DWT was applied to obtain a wavelet coefficient prism. This prism is partitioned into small code blocks with different sizes, and each subband is treated as a code block. A block splitting algorithm is used on the individual code blocks to test their significance. Compression performance was conducted on the mentioned algorithms using several AVIRIS (Airborne Visible Infrared Imaging Spectrometer) image sequences. The rate distortion performances were compared by means of the peak signal-to noise ratio (PSNR). The visual effect is considered a primary concern of hyperspectral imagery so an analysis of every algorithm's visual effect is in order. Through experimentation, AT-3DSPIHT provided high quality reconstructed images even at very low bit rate such as 0.4 bpp. A comparison was also conducted on JPEG200 and other algorithms concerning JPEG2000's multi-component images. The results have shown that AT-3DSPIHT, 3D-SPIHT and 3D-SPECK outperform JPEG2000 by the approximate range of 1 to 2.5 dB PSNR, depending on the image sequence.

Hyperspectral images are generated by collecting hundreds of narrow and contiguously spaced spectral bands of data producing a highly correlated long sequence of images. Some application specific data compression techniques may be applied advantageously before we process, store or transmit hyperspectral images. This paper applies asymmetric tree 3DSPIHT (AT-3DSPIHT) for hyperspectral image compression; it also investigates and compares the performance of the AT-3DSPIHT, 3DSPIHT and 3DSPECK on hyperspectral image compression. Results show that the AT-3DSPIHT outperforms the other two by the approximate range of 0.2 to 0.9 dB PSNR. It guarantees over 4 dB PSNR improvement at all rates or rate savings at least a factor of 2.5 over 2D coding of separate spectral bands without axial transformation.

Compressed video bitstreams require protection from channel errors in a wireless channel. The 3-D set partitioning in hierarchical trees (SPIHT) coder has proved its efficiency and its real-time capability in the compression of video. A forward-error-correcting (FEC) channel (RCPC) code combined with a single automatic-repeat request (ARQ) proved to be an effective means for protecting the bitstream. There were two problems with this scheme: (1) the noiseless reverse channel ARQ may not be feasible in practice and (2) in the absence of channel coding and ARQ, the decoded sequence was hopelessly corrupted even for relatively clean channels. We eliminate the need for ARQ by making the 3-D SPIHT bitstream more robust and resistant to channel errors. We first break the wavelet transform into a number of spatio-temporal tree blocks which can be encoded and decoded independently by the 3-D SPIHT algorithm. This procedure brings the added benefit of parallelization of the compression and decompression algorithms, and enables implementation of region-based coding. We demonstrate the packetization of the bitstream and the reorganization of these packets to achieve scalability in bit rate and/or resolution in addition to robustness. Then we encode each packet with a channel code. Not only does this protect the integrity of the packets in most cases, but it also allows detection of packet-decoding failures, so that only the cleanly recovered packets are reconstructed. In extensive comparative tests, the reconstructed video is shown to be superior to that of MPEG-2, with the margin of superiority growing substantially as the channel becomes noisier. Furthermore, the parallelization makes possible real-time implementation in hardware and software.