This paper presents a technical overview of Wide Angular Intra Prediction (WAIP) that was adopted into the test model of Versatile Video Coding (VVC) standard. Due to the adoption of flexible block partitioning using binary and ternary splits, a Coding Unit (CU) can have either a square or a rectangular block shape. However, the conventional angular intra prediction directions, ranging from 45 degrees to -135 degrees in clockwise direction, were designed for square CUs. To better optimize the intra prediction for rectangular blocks, WAIP modes were proposed to enable intra prediction directions beyond the range of conventional intra prediction directions. For different aspect ratios of rectangular block shapes, different number of conventional angular intra prediction modes were replaced by WAIP modes. The replaced intra prediction modes are signaled using the original signaling method. Simulation results reportedly show that, with almost no impact on the run-time, on average 0.31% BD-rate reduction is achieved for intra coding using VVC test model (VTM).

This paper presents the orthogonal extension of the recently introduced complementary matching pursuit (CMP) algorithm for sparse approximation. The CMP algorithm is analogous to the matching pursuit (MP) but done in the row-space of the dictionary matrix. It suffers from a similar sub-optimality as the MP. The orthogonal complementary matching pursuit algorithm (OCMP) presented here tries to remove this sub-optimality by updating the coefficients of all selected atoms at each iteration. Its development from the CMP follows the same procedure as of the orthogonal matching pursuit (OMP). In contrast with OMP, the residual errors resulting from the OCMP may not be orthogonal to all the atoms selected up to the respective iteration. Though the residual energy may increase over the OMP during the first iterations, it is shown that, compared with OMP, the convergence speed is increased in the subsequent iterations and the sparsity of the solution vector is improved.

This paper deals with spatial prediction in Wyner-Ziv video coding. Coding structures that extend DPCM to the distributed coding scenario are presented. Using a high resolution approximation, the optimal prediction filters for these structures are derived. The resulting predictive coder is employed to compress the DC component of a transform based distributed video coding system. Preliminary results show rate-distortion gains compared to the case without spatial prediction.

This paper considers the application of sparse approximations in a joint source-channel (JSC) coding framework. The considered JSC coded system employs a real number BCH code on the input signal before the signal is quantized and further processed. Under an impulse channel noise model, the decoding of error is posed as a sparse approximation problem. The orthogonal matching pursuit (OMP) and basis pursuit (BP) algorithms are compared with the syndrome decoding algorithm in terms of mean square reconstruction error. It is seen that, with a Gauss-Markov source and Bernoulli-Gaussian channel noise, the BP outperforms the syndrome decoding and the OMP at higher noise levels. In the case of image transmission with channel bit errors, the BP outperforms the other two decoding algorithms consistently.

Scalable representation of visual signals, such as image and video signals, has become a subject of active research since early 1980s. Scalability allows the adaptation of the bit rate and/or the resolution of the transmitted data to the network bandwidth and/or the rendering capability of the receiving device. For many years, spatial scalability has been achieved through wavelets, but recently the Laplacian pyramid (LP) has become an alternative choice because of reduced aliasing in the lower resolutions. In this paper, we focus on the coding efficiency of the LP with a view to transmitting it over a communication channel. In particular, we aim to improve the compression efficiency of the LP detail layers through improved interlayer prediction and orthogonal spatial transforms. First, we consider an LP in the open-loop configuration and propose to improve its rate-distortion performance by compressing it to a critically sampled representation. We derive four different orthogonal spatial transforms from the upsampling and downsampling filters that can achieve this representation, and apply them on the detail layers. The application of these transforms to the detail layers renders a fixed number of transform coefficients either zero or redundant, thus making their transmission unnecessary. Then we consider the compression of an LP in the closed-loop configuration through similar spatial transforms. Because of the introduction of quantization in the prediction loop, these spatial transforms applied on the detail layers do not produce the same number of zero or redundant transform coefficients as in the open-loop case. Nevertheless, the insight obtained from the open-loop coding leads us to enhance the interlayer prediction, and the subsequent application of the spatial transforms to the new detail layers aims to achieve better energy compaction.

In this paper, we present a novel technique for efficient compression of enhancement layers in scalable video coding (SVC). First we propose an improved interlayer prediction scheme which exploits the inherent redundancy of the underlying Laplacian pyramid with nonbiorthogonal filters. Secondly we introduce an orthogonal transform in parallel with the current 4×4 transform to improve the coding efficiency of the enhancement layer further. The improved prediction and the transform are implemented in the SVC reference software JSVM 4.0 as additional prediction modes. Experimental results demonstrate coding gains up to 1 dB for I pictures, and up to 0.7 dB for both I and P pictures, over a current implementation.

This paper presents an improved intra prediction scheme for H.264/AVC scalable extension. First, a more flexible intra prediction is realized by introducing a sub-macroblock (sub-MB) level inter-layer intra prediction. Second, the upsampled reconstructed base layer helps to reduce the number of candidate modes for the spatial intra prediction. Finally, the neighboring pixels of the reconstructed enhancement layer are used to improve the quality of the reference from the base layer by a simple cross boundary Alter. Our scheme improves the PSNR up to 0.53 dB when compared with the JSVM standard implementation for all intra coding.

Recently the Laplacian pyramid (LP) has come to prominence as a tool for obtaining spatial scalability in the context of scalable image and video compression. In this paper, we present a critical representation scheme for the LP. The representation is obtained by transforming the detail signals to lower dimensional representations. Unlike the wavelet transform, the proposed transform depends on the decimation and the interpolation filters used for the classical LP. Simulation results suggest that higher compression ratios can be achieved with the critical representation than with the standard LP with usual or frame based reconstructions.

The Laplacian pyramid (LP) is one of the earliest examples of multiscale representation of visual data. It is well known that an LP is overcomplete or redundant by construction, and has lower compression efficiency compared to critical representations such as wavelets and subband coding. In this paper, we propose to improve the rate-distortion (R-D) performance of the LP through critical representation. We consider an LP with biorthogonal decimation and interpolation filters, and show that the detail signals lie in lower-dimensional subspaces. This allows them to be represented using fewer coefficients than the original spatial representations. We derive orthogonal bases for these subspaces and represent the detail signals in terms of their projections onto these bases. Simulation results suggest that higher compression ratios can be achieved with the critical representation than with the standard LP with usual or dual frame based reconstructions

Recently, a class of real-number Bose-Chaudhuri-Hocquengem codes known as discrete Fourier transform (DFT) codes have been considered as joint source and channel codes for providing robustness to erasures and errors over wireless networks. We propose three subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC, the minimum-norm, and the ESPRIT algorithms used in array signal processing for direction-of-arrival estimation. They provide different but related formulations of the error localizations by first partitioning a vector space into the channel error subspace and its orthogonal complement, the noise subspace. The locations of the errors are determined from either the error subspace eigenvectors or the noise subspace eigenvectors. We also present a brief performance analysis of the localization error in terms of the perturbation of the error subspace due to quantization. Simulation results show that their localization performances are similar, and they perform better than the coding-theoretic approach over a broad range of channel-error-to-quantization-noise ratios.

Joint source and channel coding of multimedia data for transmission over wireless networks is a challenging task. Recently, a class of BCH-like real number codes has been considered for providing robustness to data loss or data error over such networks. Unlike finite-field codes, the real codevectors undergo a quantization process before being transmitted, and therefore, the error localization efficiency becomes algorithm-dependent. The paper presents an ESPRIT-like subspace algorithm for localizing errors in such codes and compares its performance with other known localization algorithms. In particular, we consider lowpass DFT, DCT and DST codes, and compare the localization efficiency of the ESPRIT-like algorithm with the standard BCH decoding approach and the MUSIC-like subspace approach. Simulation results with a Gauss-Markov source are presented.

We propose two subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC and the minimum-norm algorithms employed in array signal processing for direction of arrival (DOA) estimation. We present the algorithms in a generalized form with a variable dimension syndrome covariance matrix. Simulation results show that their localization performances are similar, and they achieve the peak values when the rank of the covariance matrix reaches the maximum value. They also perform better than the coding theoretic approach over a broad range of channel-error-to-quantization-noise ratio.

A subspace based method for real error correction with low-pass DFT codes is proposed. The common roots of the eigen-polynomials of the syndrome covariance matrix associated with the zero eigenvalue gives the error locations. When the codevectors are quantized, the localization algorithm is modified so as to minimize the quantization noise effects. The proposed method is analogous to the subspace based spectral estimation techniques in array signal processing, but adapted to discrete error locations and only one set of syndrome coefficients. By combining the analytical results for perfect error localization with the subspace based approach, we obtain superior results than the existing approach.

In this paper, we propose a subspace algorithm for error localization with quantized frame expansion. The algorithm is similar to the MUSIC algorithm for direction of arrival (DOA) estimation in the array signal processing. The frames considered are a class of error-correcting frames characterized by the BCH-like property of the parity check matrices of the associated complex block codes. In particular, we consider low-pass DFT, DCT and DST frames. Simulation results show that the subspace algorithm outperforms the error locator polynomial approach in localization efficiency. For a G-M source, the DFT frame is seen to have the best performance, however, when applied to an image, the DCT and DST frames are seen to perform somewhat better than the DFT frame especially at higher channel bit error rates.

This paper deals with the decoding of lowpass DFT codes in presence of both errors and erasures. First we consider the case when the codevectors are not quantized and present a coding theoretic algorithm as well as subspace based decoding algorithm. Then we consider the quantization of the codevectors and show the equivalence between the error and erasure correction, and the estimation of directions of arrival (DOA) of plane waves in array signal processing. This analogy leads to the introduction of an adaptation of the subspace based DOA estimation techniques to the problem of error and erasure correction in the real field. The adaptation incorporates a whitening operation, which is shown to be equivalent to the projection of the syndrome vector onto the orthogonal complement of the subspace spanned by the erasure locator vectors. Simulation results with a Gauss-Markov source reveal that the proposed subspace based algorithm is more efficient than the coding theoretic approach in localizing the errors.

In this paper, we analyze the performance of DFT codes with bursty erasures in the framework of syndrome decoding. Bursty erasures give rise to a syndrome decoding matrix which has very large elements depending on the code parameters and the burst length. The largeness of the elements of the syndrome decoding matrix is studied by establishing various relationships between the syndrome decoding matrix, and the parity and the generator polynomial coefficients. With a suitable model for the quantization error, the reconstruction error performance of DFT codes in the context of the syndrome decoding is analyzed and then applied to the case of bursty erasures. Simulation results with a Gauss-Markov source verify the theoretical results obtained with the assumed quantization error model.

Recently DFT codes have been considered for use as joint source-channel codes in order to provide robustness against packet loss in IP networks. In this paper, we propose a recursive syndrome decoding algorithm of DFT codes for bursty erasure patterns. The algorithm is based on the statistical properties of the quantization error and the codevector. The statistical analysis of the codevector is independent of the proposed algorithm. We also analyze the error performance of the proposed decoding scheme, and show that it can perform better than the conventional syndrome decoding of DFT codes if the message signal model and quantization error model assumptions are valid. Simulation results with a Gauss-Markov source are presented.

This paper deals with the error localization performance analysis of low-pass DFT codes in presence of quantization noise. The performance is evaluated in terms of the magnitude of the error locator polynomial computed at the error locations. It is shown that the mean squared magnitude is a function of the channel error-to-quantization noise ratio and the relative locations of the channel errors.

Discrete Fourier transform (DFT) codes are used to provide robustness against packet losses in IP networks. First, the relationship between DFT codes and over-complete expansions using frames is established. It is shown that the message recovery by syndrome decoding of erasures and direct signal space projection from received samples are equivalent. The error performance analysis of DFT codes is used to develop packetization criteria in order to guarantee minimum reconstruction error.