Rate-distortion Optimization Procedure Research Articles

Improving compression efficiency of HEVC using perceptual coding

The introduction of the new coding tools in HEVC has brought significant bitrate savings compared to the previous standard, H.264/AVC. In both standards, the mean square error (MSE) is used for measuring distortion in the rate distortion optimization process of the coding unit structure and mode selection. However, MSE is not a good measure to use for measuring visual quality as it poorly correlates with human perception. Integration of a video quality metric based on the characteristics of the Human Visual System (HVS) inside the rate distortion optimization procedure is expected to improve the compression efficiency of video coding. In this paper, the PSNR-HVS measure is used in the rate distortion optimization process for the coding unit structure and mode selection. In the first step, we find the scaling factor for the Lagrangian multiplier based on the proposed perceptual approach. In the second step, we find optimal Lagrangian multiplier depending on the quantization parameter. The compression efficiency of the proposed approach is compared to that of HEVC. Simulations prove that the proposed approach yields higher compression efficiency.

Perceptually Adaptive Lagrangian Multiplier for HEVC Guided Rate-Distortion Optimization

Recent video coding standards typically use the Rate-distortion optimization (RDO) method, which is essential to appropriately perform mode decisions during encoding process. The newest standard high efficiency video coding (HEVC) introduces complex encoding structures and strong dependency between coding units. Particularly, the Lagrangian multiplier is a primary factor in RDO procedure, which directly affects the rate-distortion (R-D) performance and is defined for an entire video frame. This paper proposes a novel approach for perceptually guiding the RDO process in HEVC. The reference encoder does not consider effectively the perceptual characteristics of the input video and further, the visual sensitivity of each coding tree unit (CTU) in a frame. Inspired by the mechanisms of the human visual system, the proposed solution is a CTU-level adjustment of Lagrangian value based on a set of complementary perceptual features. The proposed scheme concerns important visual information of a CTU and its temporal dependency with adjacent blocks. Feature extraction is implemented in the frequency domain using efficient spatio-temporal analysis. In our experiments, we opted a perceptual mean squared error (MSE) metric and structural similarity (SSIM) index. According to perceptual MSE metric, the BD-rate savings using the Bjontegaard delta measurements, were fairly convincing over the state-of-the-art HEVC software HM16.12; 4.41% and 6.14% for random access (RA) and low delay (LD) encoding settings, respectively. Using SSIM, the BD-Rate achieved 6.95% and 9.86% for RA and LD settings, respectively. The proposed method further demonstrates a superior R-D performance over a compared approach adopting a similar scheme.

Multi-directional Mode Reduction Algorithm for Intra Prediction in Video Coding

ABSTRACTThe recent digital video-coding standard achieves very high data compression by using several new coding features, such as intra/inter predictions, variable block size motion compensation, multiple motion vectors, quarter pixel for motion compensation and so on. Among all the features, one of the key features is intra prediction, which is based on the rate-distortion optimization (RDO) procedure that requires a true encoding of the intra block by trying all intra prediction modes. In this paper, we propose a multi-directional mode reduction (MDMR) algorithm using the prediction error to reduce the number of coding modes required for the RDO procedure. The proposed MDMR algorithm effectively estimates the prediction error of the block to narrow down the predictive modes to reduce RDO computation. Experimental results show that the proposed algorithm achieves a time reduction of more than 64% with negligible PSNR loss and bitrate increment.

Two-step regionally focused algorithm for H.264/AVC encoder

H.264, MPEG-4 Part 10, is the latest digital video coding standard that achieves very high data compression by using several new coding features. One of the key features is the intra prediction based on the rate-distortion optimization (RDO) procedure, which requires a true encoding of the intra block by trying all intra prediction modes. The computational burden of intra prediction with the RDO procedure is extremely high for real-time applications. In this paper, we propose a classified block type algorithm that selects the appropriate intra block size and further our proposed two-step regionally focused algorithm selected block that reduces a number of candidate mode required for RDO which significantly reduces the encoder complexity. Experimental results show that the proposed fast mode decision algorithm reduces the encoding time by about 68%, with negligible loss of video quality.

Joint redundant motion vector and intra macroblock refreshment for video transmission

Abstract This paper proposes a scheme for error-resilient transmission of videos which jointly uses intra macroblock refreshment and redundant motion vector. The selection of using intra refreshment or redundant motion vector is determined by the rate-distortion optimization procedure. The end-to-end distortion is used for the rate-distortion optimization, which can be easily calculated with the recursive optimal per-pixel estimate (ROPE) method. Simulation results show that the proposed method outperforms both the intra refreshment approach and redundant motion vector approach significantly, when the two approaches are deployed separately. Specifically, for the Foreman sequence, the average PSNR of the proposed approach can be 1.12 dB higher than that of the intra refreshment approach and 5 dB higher than that of the redundant motion vector approach.

JPEG2000-Based Scalable Interactive Video (JSIV)

We propose a novel paradigm for interactive video streaming and we coin the term JPEG2000-based scalable interactive video (JSIV) for it. JSIV utilizes JPEG2000 to independently compress the original video sequence frames and provide for quality and spatial resolution scalability. To exploit interframe redundancy, JSIV utilizes prediction and conditional replenishment of code-blocks aided by a server policy that optimally selects the number of quality layer for each code-block transmitted and a client policy that makes most of the received (distorted) frames. It is also possible for JSIV to employ motion compensation; however, we leave this topic to future work. To optimally solve the server transmission problem, a Lagrangian-style rate-distortion optimization procedure is employed. In JSIV, a wide variety of frame prediction arrangements can be employed including hierarchical B-frames of the scalable video coding (SVC) extension of the H.264/AVC standard. JSIV provides considerably better interactivity compared to existing schemes and can adapt immediately to interactive changes in client interests, such as forward or backward playback and zooming into individual frames. Experimental results for surveillance footage, which does not suffer from the absence of motion compensation, show that JSIV's performance is comparable to that of SVC in some usage scenarios while JSIV performs better in others.

Three-Dimensional SPIHT Coding of Volume Images with Random Access and Resolution Scalability

End users of large volume image datasets are often interested only in certain features that can be identified as quickly as possible. For hyperspectral data, these features could reside only in certain ranges of spectral bands and certain spatial areas of the target. The same holds true for volume medical images for a certain volume region of the subject's anatomy. High spatial resolution may be the ultimate requirement, but in many cases a lower resolution would suffice, especially when rapid acquisition and browsing are essential. This paper presents a major extension of the 3D-SPIHT (set partitioning in hierarchical trees) image compression algorithm that enables random access decoding of any specified region of the image volume at a given spatial resolution and given bit rate from a single codestream. Final spatial and spectral (or axial) resolutions are chosen independently. Because the image wavelet transform is encoded in tree blocks and the bit rates of these tree blocks are minimized through a rate-distortion optimization procedure, the various resolutions and qualities of the images can be extracted while reading a minimum amount of bits from the coded data. The attributes and efficiency of this 3D-SPIHT extension are demonstrated for several medical and hyperspectral images in comparison to the JPEG2000 Multicomponent algorithm.

Image compression using an edge adapted redundant dictionary and wavelets

Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The advantages of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that in 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. In this paper we propose an approach based on building a sparse representation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-terms non-linear approximations in general dictionaries is, in most cases, a NP-hard problem and sub-optimal approaches have to be followed. In this work we use a greedy strategy, also known as Matching Pursuit to compute the expansion. The residual, that we suppose to be the smooth and texture part, is then coded using wavelets. A rate distortion optimization procedure chooses the number of functions from the redundant dictionary and the wavelet basis.

Multidimensional signal compression using multiscale recurrent patterns

In this paper we propose a new multidimensional signal lossy compression method based on multiscale recurrent patterns, referred to as multidimensional multiscale parser (MMP). In it, a multidimensional signal is recursively segmented into variable-length vectors, and each segment is encoded using expansions and contractions of vectors in a dictionary. The dictionary is updated while the data is being encoded, using concatenations of expanded and contracted versions of previously encoded vectors. The only data encoded are the segmentation tree and the indexes of the vectors in the dictionary, and therefore no side information is necessary for the dictionary updating. The signal segmentation is carried out through a rate–distortion optimization procedure. A two-dimensional version of the MMP algorithm was implemented and tested with several kinds of image data. We have observed that the proposed dictionary updating procedure is effective in adapting the algorithm to a large variety of image content, lending to it a universal flavor. For text and graphics images, it outperforms the state-of-the-art SPIHT algorithm by more that 3 dB at 0.5 opp , while for mixed document images, containing text, graphics and gray-scale images, by more than 1.5 dB at the same rate. Due to the way the images are segmented, they appear slightly blocky at low rates. We have alleviated this problem by proposing an effective way of reducing the blockiness in the reconstructed image, with no penalty in signal-to-noise ratio performance in most cases. We conclude the paper with a theoretical analysis of the approximate matching of Gaussian vectors using scales, which gives a justification of why approximate multiscale matching is a good option, specially at low rates.

Quantizacao Vetorial Adaptativa Multiescalas com Otimização Taxa-Distorção

In this paper we propose a new multidimensional lossy sigual compression method based on multiscale recurrent patterns, referred to as MMP (Multidimensional Multiscale Parser). In it, a multidimensional signal is recursively segmented into variable-length vectors, and each segment is encoded using expansions and contractions of vectors in a dictionary. The dictionary is updated while the data is being encoded, using concatenations of expanded and contracted versions of previously encoded vectors. The only data encoded are the segmentation tree and the indexes of the vectors in the dictionary, and therefore no side information is necessary for the dictionary updating. The signal segmentation is carried out through a rate-distortion optimization procedure. A two-dimensional version of the MMP algorithm was implemented and tested with several kinds of image data. We have observed that the proposed dictionary updating procedure is effective in adapting the algorithm to a large variety of image content, lending to it a universal flavor. For text and graphics images, it outperforms the state-of-the-art SPIHT algorithm by more that 3dB at 0.5bpp, while for mixed document images, containing text, graphics and gray scale images, by more than 1.5dB at the same rate. We conclude the paper with a theoretical analysis of the approximate matching of gaussian vectors using scales, which gives a justification of why approximate multiscale matching is a good option, speciallyat low rates.