Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

High Efficiency Video Coding (HEVC) is the new video coding standard recently approved by ISO and ITU. HEVC allows a bit rate reduction greater than a 50% with respect to its predecessor, the H.264/AVC, offering the same perceptual quality, by means of a set of new tools that have been introduced. Compared with the current state of the art in image coding, such as JPEG, JPEG2000 or the new JPEG XR, the new Intra Frame coding performs a high compression process in the "All-Intra" mode. All these improvements are at expense of a high computational cost, making it considerably difficult to implement in real time. Hence, this paper presents a mechanism that can be used by the RDO algorithm to select the optimal coding block size for Intra-Prediction, by using a data mining classifier, based on a previous training. Experimental results show that the proposed algorithm can achieve a 30% of Time Savings over a wide range of high resolution sequences (Class A, B and F), with a negligible loss of coding efficiency.

This is a MM'14 Workshop Summary Abstract for PIVP'14 - 1st International Workshop on Perception Inspired Video Processing. Workshop provided a venue for researchers involved with perceptual video processing and coding to present their current work and to discuss future directions. The workshop program consisted of a keynote talk, oral presentations of full papers and interactive poster session for the short papers. All participants took part in the discussion panel at the end of workshop, exchanging ideas and suggestions for future work.

Wyner-Ziv video coding paradigm provides a framework where most of complexity is moved from the encoder to the decoder. In this way, Wyner-Ziv coding support efficiently multimedia services for low complexity devices which have to capture, encode and send video. Aplications such as mobile phones, sensor, Personal Digital Assistant (PDA) could benefice to this paradigm. On contraty to tradicional video paradigms, the complexity of the decoder is quite high and it should be reduced. This work presents a parallel Wyner-Ziv decoding algorithm in aims to reduce its high complexity. The present approach efficiently distribute the burden of the complexity over the number of cores which are available in the architecture. By using this parallel approach, the decoding time is reduced around 73%. The proposed methods are scalable for any multicore architecture and adaptable for different Wyner-Ziv decoding schemes.

H.264/AVC is the commercial standard currently most in use for video and is based on single view (mono view). Recently, the video community has also standardized an H.264/AVC extension for supporting 3D video sensation which is referred to as Multiview Video Coding (MVC). Like H.264/AVC, MVC includes temporal and spatial prediction but also includes inter-view prediction as well as disparity estimation. Until now, in H.264/AVC the inter prediction techniques have been the most time-consuming tasks and, thus, in MVC with its new interview predictions the encoding time is even higher. This paper proposes a GPU-based algorithm for both temporal and interview prediction. The algorithm proposed is able to perform this complex prediction task by means of an efficient distribution of all the computations over the GPU and also tries to mitigate the sequential dependencies. The approach can achieve a remarkable time reduction of up to 98% with only a negligible loss in coding efficiency. Moreover, this paper shows that the proposed GPU-based algorithm is more energy efficient and thus, requires less energy than the sequential reference.

SUMMARY H.264/MPEG‐4 part 10 is the latest standard for video compression and promises a significant advance in terms of quality and distortion compared with the commercial standards currently most in use such as MPEG‐2 or MPEG‐4. To achieve this better performance, H.264 adopts a large number of new/improved compression techniques compared with previous standards, albeit at the expense of higher computational complexity. In addition, in recent years new hardware accelerators have emerged, such as graphics processing units (GPUs), which provide a new opportunity to reduce complexity for a large variety of algorithms. However, current GPUs suffer from higher power consumption requirements because of its design. Up to now, GPU‐based software developers have not taken this into account. In this paper, we present a detailed procedure to implement the H.264 motion estimation for a GPU, with the aim of reducing time and, as a consequence, the energy consumption. The results show a negligible drop in rate distortion with a time reduction of over 91.5% on average and it reduces the energy consumption by a factor of 11.78 compared with the reference implementation. Copyright © 2011 John Wiley & Sons, Ltd.

Wyner-Ziv video coding presents a new paradigm which offers low-complexity video encoding. However, the Wyner-Ziv paradigm accumulates high complexity at the decoder side and this could involve difficulties for applications which have delay requisites. On the other hand, technological advances provide us with new hardware which supports parallel data processing. In this paper, a faster Wyner-Ziv video decoding scheme based on multicore processors is proposed. In this way, each frame is decoded by means of the collaboration between several processing units, achieving a time reduction up to 71% without significant rate-distortion drop penalty.

H.264/AVC applies a complex mode decision technique that has high computational complexity in order to reduce the temporal redundancies of video sequences. Several algorithms have been proposed in the literature in recent years with the aim of accelerating this part of the encoding process. Recently, with the emergence of many-core processors or accelerators, a new approach can be adopted for reducing the complexity of the H.264/AVC encoding algorithm. This paper focuses on reducing the inter prediction complexity adopted in H.264/AVC and proposes a GPU-based implementation using CUDA. Experimental results show that the proposed approach reduces the complexity by as much as 99% (100x of speedup) while maintaining the coding efficiency.

Distributed Video Coding (DVC) provides a new coding paradigm based on lower complex encoders than decoders. On the decoder side some missed frames have to be estimated by means of available frames and a correlation noise model. In addition, parity bit chunks can be requested to the encoder across the feedback channel to correct the mismatches of these frames. This is an iterative procedure which collets most of the complexity of the decoder. In this work, a novel approach is proposed to parallelize the DVC decoding process in a multicore system. In this way, each bitplane is decoded at the same time by a different core and they exchange information to update the integration limits of the probably model, reaching a time reduction up to 80% with a little bitrate penalty but maintaining the same PSNR.

Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format and the distribution of the motion compensated residual in MPEG-2. Moreover, a dynamic motion estimation technique is also proposed to further speed-up the decision process. Finally, we go a step further on our previous research efforts by combining the two aforementioned speed-up approaches. Our simulation results over more than 40 sequences at common intermediate format and quarter common intermediate format resolutions show that our proposal outperforms the MB mode selection of the rate-distortion optimization option of the H.264 encoder process by reducing the computational requirements by up to 90%, while maintaining the same coding efficiency. Finally, we conduct a comparative study of our approach with the most relevant fast inter-prediction methods for MPEG-2 to H.264 transcoder recently reported in the literature.

This paper proposes an improved Wyner-Ziv to H.264 video transcoder as part of a framework for mobile to mobile video communications. In this scheme, the user devices keep the low complexity constraints by using the Wyner-Ziv encoding and H.264 decoding algorithms. They shift their complexities to the network where the proposed transcoder is allocated. The main goal of the transcoder is to convert the bitstream and reduce the delay efficiently. The results show that the proposed transcoder reduces the complexity by a factor of 95% with a negligible rate-distortion loss.

The authors develop a decoder/encoder system (transcoder) to solve the consumption constraint in the communications between end-user devices, when a new Wyner–Ziv (WZ)/H.264 framework is defined for being used in mobile-to-mobile environments. This approach is based on leaving to the devices only WZ video encoding and traditional video decoding; the lowest complexity algorithms in both paradigms. The system shifts the burden of complexity to the network, where an improved transcoder that reuses information between both paradigms is allocated. The WZ decoding motion vectors are used to reduce the H.264 motion estimation process. The proposed transcoder offers a complexity reduction up to 60% on average, without any rate distortion drop.

Most of the reported distributed video coding (DVC) architectures have a serious limitation that hinders its practical application. The uses of a feedback channel between the encoder and the decoder require an interactive decoding procedure which is a limitation for applications such as offline processing. On the other hand, the decoder needs an efficient way to estimate the probability of error without assuming the availability of the original video at the decoder. In this paper we continue with our previous works into a more practical DVC architecture which solves both problems based on the use of machine learning. The proposed approach is based on extracting the relationships that exist between the residual frame and the number of requests over this feedback channel. We apply these concepts to pixel-domain Wyner-Ziv coding demonstrating significant savings in bitrates with a little loss of quality.

Distributed video coding has become increasingly popular in recent years among the researchers in video coding due to its attractive and promising features. DVC proposed a dramatic structural change to video coding by shifting the majority of complexity conventionally residing in the encoder towards the decoder. Nevertheless, these kinds of architectures have some serious limitations that hinder its practical application. The uses of a feedback channel between the encoder and the decoder requires an interactive decoding procedure which is a limitation for certain applications such as offline processing. On the other hand, the decoder needs an efficient way to estimate the probability of error without assuming the availability of the original video at the decoder. In this paper we investigate a first approximation to solve both problems based on the use of machine learning to extract the knowledge that exits between the residual frame and the number of requests over this feedback channel. Exploiting this correlation gives us a more practical architecture without higher complexity encoders. We apply these concepts to pixel-domain Wyner-Ziv coding and the results show a loss of 0.21 dB in the rate-distortion performance.

The H.264 standard achieves much higher coding efficiency than the MPEG-2 standard, due to its improved inter-and intra-prediction modes at the expense of higher computational complexity. Transcoding MPEG-2 video to H.264 is important to enable gradual migration to H.264. However, given the significant differences between the MPEG-2 and the H.264 coding algorithms, transcoding is a much more complex task and new approaches to transcoding are necessary. The main problems that need to be addressed in the design of an efficient heterogeneous MPEG-2/H.264 transcoder are: the inter-frame prediction, the transform coding and the intra-frame prediction. In this paper, we focus our attention on the inter-frame prediction, the most computationally intensive task involved in the transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for P-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast MB mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and construct decision trees to classify the incoming MPEG-2 MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the MB mode selection complexity by as much as 95% while maintaining the coding efficiency. Finally, we conduct a comparative study with some of the most prominent fast inter-prediction methods for H.264 presented in the literature. Our results show that the proposed approach achieves the best results for video transcoding applications.

Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications.

In this paper, we propose the use of data mining algorithms to create a macroblock partition mode decision algorithm for inter-frame prediction, to be used as part of a high-efficient H.263 to H.264 transcoder. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming H.263 MC residual into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the inter-prediction complexity by as much as 60% while maintaining the coding efficiency.

The two key problems in video transcoding are complexity reduction and quality management. The complexity of H.264 encoding makes complexity reduction even more important. A good transcoder design reduces complexity with negligible loss in quality. While motion estimation complexity can be easily reduced by reducing the search range or eliminating the coding modes, these approaches will cause significant quality drop. This paper presents reduced complexity motion estimation for MPEG-2 to H.264 transcoding. The proposed approach uses MPEG-2 motion vectors to dynamically reduce the search range and the motion vector refinement window. This approach is based on the hypothesis that longer MPEG-2 motion vectors indicate higher motion and hence a larger search window as well as a larger refinement window is necessary. Experimental results show that the proposed approach reduces the motion estimation complexity by as much as 85% with negligible loss in PSNR. We show that the results hold across video resolutions and bitrates.

Though H.264 is highly efficient compared to MPEG-2, the wide and deep penetration of MPEG-2 creates a need for co-existence of these technologies and hence creates a need for transcoding. In this paper, we introduce and evaluate a novel macroblock partition mode decision algorithm for inter-frame prediction to be used as part of a high-efficient MPEG-2 to H.264 transcoder. The proposed tools are used to compute an optimal MB coding mode decision with significantly reduced computational complexity. Specifically, we achieve the computational savings by using the following MB information coming from MPEG-2: the MB coding modes in MPEG-2 and the mean and variance of the 16 4×4 sub blocks of the MPEG-2 residual MBs. We use data mining algorithms to develop a decision tree for obtaining the H.264 coding decision modes. Our results show that the proposed algorithm is able to maintain a good picture quality while considerably reducing the number of operations to be performed.

In this paper, we introduce and evaluate a low complexity macroblock partition mode decision algorithm for inter-frame prediction in MPEG-2 to H.264 transcoder. The proposed tools are used to compute an optimal MB coding mode decision with significantly reduced computational complexity. Specifically, we achieve the computational savings by using the following MB information coming from MPEG-2: the MB coding modes, the coded block pattern (CBPC) in MPEG-2, and the mean and variance of the 16 4times4 sub blocks of the MPEG-2 residual MBs. We use data mining algorithms to develop a decision tree for H.264 coding mode decisions. The decision trees are built using RD optimized mode decisions and result in highly efficient mode decisions. The proposed transcoder is 35% faster than the RD optimized H.264 reference transcoder without a significant PSNR degradation. The proposed transcoder performs over 3 dB better than the SAE cost based H.264 transcoding

The H.264 standard, jointly developed by the ITU-T and the MPEG committees, is highly efficient offering perceptually equivalent quality video at about 1/3 to 1/2 of the bitrates offered by the MPEG-2 format. These significant bandwidth savings open the market to new products and services, including HDTV services at lower bitrates. It is expected that the H.264/AVC takes over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm takes several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for transcoding technologies for converting the large volume of existent video material from the MPEG-2 into the H.264 format and vice versa. However, given the significant differences between the MPEG-2 and the H.264 encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this paper, we introduce and evaluate a novel fast intra-frame prediction algorithm to be used as part of a high-efficient MPEG-2 to H.264 transcoder. Our results show that the proposed algorithm is able to maintain a good picture quality while considerably reducing the number of operations to be performed.

MPEG-2 is the most widely digital video-encoding standard in use nowadays. It is being widely used in the development and deployment of digital TV services, DVD and video-on-demand services. However, recent developments have given birth to the H.264/AVC, offering better bandwidth to video quality ratios than MPEG2. It is expected that the H.264/AVC will take over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm will take several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for transcoding technologies for converting the large volume of existent video material from the MPEG-2 into the H.264 format and vice versa. However, given the significant differences between the MPEG-2 and the H.264 encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this paper, we introduce and evaluate a novel intra-frame prediction algorithm to be used as part of a high-efficient MPEG-2 to H.264 transcoder. Our evaluation results show that the proposed algorithm considerable reduces the complexity involved in the intra-frame prediction: a key operation in the transcoding process