An Efficient Algorithm for VC-1 to H.264 Video Transcoding in Progressive Compression

The high definition video adoption has been growing rapidly for the last two years. The two high definition DVD formats HD-DVD and Blueray have mandated MPEG-2, H.264 and VC-1 as video compression formats. The coexistence of these different video coding standards creates a need for transcoding. In this paper, an efficient transcoding algorithm from VC-1 video to H.264 video is discussed. While there has been recent work on MPEG-2 to H.264 transcoding, the published work on VC-1 to H.264 transcoding is non-existent. There is very limited amount of published work on VC-1. This paper gives a brief overview of VC-1 and discusses the opportunities for low-complexit

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

Study of a Framework For Video Streaming In Mobile Devices (AMoV and ESoV)

AMoV (adaptive mobile video streaming) and ESoV(efficient social video sharing) are the terms which are currently gaining the attention of variety of computer users and researchers. While enjoying the multimedia services like videos and images, the basic quandary faced by any individual is the progressive downloading or the buffering of the videos. As the researches are focusing on various technologies in said issue, very least focus is given on to the security issues present in these technologies. The basic idea behind this paper is to study and to survey the literature and to propose the security aspects in related field

A Cost Shared Quantization Algorithm and its Implementation for Multi-Standard Video CODECS

The current trend of digital convergence creates the need for the video encoder and decoder system, known as codec in short, that should support multiple video standards on a single platform. In a modern video codec, quantization is a key unit used for video compression. In this thesis, a generalized quantization algorithm and hardware implementation is presented to compute quantized coefficient for six different video codecs including the new developing codec High Efficiency Video Coding (HEVC). HEVC, successor to H.264/MPEG-4 AVC, aims to substantially improve coding efficiency compared to AVC High Profile. The thesis presents a high performance circuit shared architecture that can perform the quantization operation for HEVC, H.264/AVC, AVS, VC-1, MPEG- 2/4 and Motion JPEG (MJPEG). Since HEVC is still in drafting stage, the architecture was designed in such a way that any final changes can be accommodated into the design. The proposed quantizer architecture is completely division free as the division operation is replaced by multiplication, shift and addition operations. The design was implemented on FPGA and later synthesized in CMOS 0.18 μm technology. The results show that the proposed design satisfies the requirement of all codecs with a maximum decoding capability of 60 fps at 187.3 MHz for Xilinx Virtex4 LX60 FPGA of a 1080p HD video. The scheme is also suitable for low-cost implementation in modern multi-codec systems

Rinnakkainen toteutus H.265 videokoodaus standardille

The objective of this study was to research the scalability of the parallel features in the new H.265 video compression standard, also know as High Efficiency Video Coding (HEVC). Compared to its predecessor, the H.264 standard, H.265 typically achieves around 50% bitrate reduction for the same subjective video quality. Especially videos with higher resolution (Full HD and beyond) achieve better compression ratios. Also a better utilization of parallel computing resources is provided. H.265 introduces two novel parallelization features: Tiles and Wavefront Parallel Processing (WPP). In Tiles, each video frame is divided into areas that can be decoded without referencing to other areas in the same frame. In WPP, the relations between code blocks in a frame are encoded so that the decoding process can progress through the frame as a front using multiple threads. In this study, the reference implementation for the H.265 decoder was augmented to support both of these parallelization features. The performance of the parallel implementations was measured using three different setups. From the measurement results it could be seen that the introduction of more CPU cores reduced the total decode time of the video frames to a certain point. When using the Tiles feature, it was observed that the encoding geometry, i.e. how each frame was divided into individually decodable areas, had a noticeable effect on the decode times with certain thread counts. When using WPP, it was observed that what was mostly synchronization overhead, sometimes had a negative effect on the decode times when using larger (4-12) amounts of threads.Tämän tutkimuksen aiheena oli tutkia uuden H.265 videonpakkausstandardin (tunnetaan myös nimellä HEVC (engl. High Efficiency Video Coding)) rinnakkaisuusominaisuuksien skaalautuvuutta. Verrattuna edeltäjäänsä, H.264 videonpakkaustandardiin, H.265 tyypillisesti saavuttaa samalla kuvanlaadulla noin 50% pienemmän pakkauskoon. Erityisesti suuren resoluution videoilla (Full HD ja suuremmat) pakkaustehokkuuden paremmuus korostuu. Huomiota on kiinnitetty myös moniydinprosessoreiden hyödyntämiseen videokoodauksessa. H.265 tarjoaa kaksi uutta rinnakkaisuusominaisuutta: niin kutsutut Tiles- ja WPP-menetelmät (engl. \emph{Wavefront Parallel Processing}). Tiles-menetelmässä jokainen videon kuva jaetaan alueisiin, jotka voidaan purkaa viittaamatta saman kuvan muihin alueisiin. WPP-menetelmässä suhteet kuvan lohkoihin pakataan siten että purkamisprosessi pystyy etenemään kuvan läpi rintamana hyödyntäen useampia säikeitä. Tässä tutkimuksessa H.265 videodekooderin referenssitoteutusta laajennettiin tukemaan molempia näistä rinnakkaisuusominaisuuksista. Suorituskykyä mitattiin käyttäen kolmea eri mittausasetelmaa. Mittaustuloksista ilmeni, että prosessoriydinten lukumäärän kasvattaminen nopeutti videoiden purkamista tiettyyn pisteeseen asti. Tiles-menetelmää mitatessa havaittiin, että alueiden geometrialla, eli kuinka kuva jaettiin riippumattomiin alueisiin, on huomattava vaikutus purkamisnopeuteen tietyillä säiemäärillä. WPP-menetelmää mitattaessa havaittiin että korkeampiin säiemääriin (4-12) siirryttäessä purkamisnopeus alkoi hidastua. Tämä johtui pääasiassa säikeiden keskinäiseen synkronointiin kuluvasta ajasta

Seamless multimedia delivery within a heterogeneous wireless networks environment: are we there yet?

The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users’ quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided

Platforms for handling and development of audiovisual data

Estágio realizado na MOG Solutions e orientado por Vítor TeixeiraTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Optimization of the motion estimation for parallel embedded systems in the context of new video standards

15 pagesInternational audienceThe effciency of video compression methods mainly depends on the motion compensation stage, and the design of effcient motion estimation techniques is still an important issue. An highly accurate motion estimation can significantly reduce the bit-rate, but involves a high computational complexity. This is particularly true for new generations of video compression standards, MPEG AVC and HEVC, which involves techniques such as different reference frames, sub-pixel estimation, variable block sizes. In this context, the design of fast motion estimation solutions is necessary, and can concerned two linked aspects: a high quality algorithm and its effcient implementation. This paper summarizes our main contributions in this domain. In particular, we first present the HME (Hierarchical Motion Estimation) technique. It is based on a multi-level refinement process where the motion estimation vectors are first estimated on a sub-sampled image. The multi-levels decomposition provides robust predictions and is particularly suited for variable block sizes motion estimations. The HME method has been integrated in a AVC encoder, and we propose a parallel implementation of this technique, with the motion estimation at pixel level performed by a DSP processor, and the sub-pixel refinement realized in an FPGA. The second technique that we present is called HDS for Hierarchical Diamond Search. It combines the multi-level refinement of HME, with a fast search at pixel-accuracy inspired by the EPZS method. This paper also presents its parallel implementation onto a multi-DSP platform and the its use in the HEVC context