Video Compression Algorithms for Transmission and Video.

The area of image and video compression has made tremendous progress over the last several decades. The successes in image compression are due to advances and better understanding of waveform coding methods which take advantage of the signal statistics, perceptual methods which take advantage of psychovisual properties of the human visual system (HVS) and object-based models especially for very low bit rate work. Recent years have produced several image coding standards—JPEG for still image compression and H.261, MPEG-I and MPEG-II for video compression. While we have devoted a special section in this paper to cover international coding standards because of their practical value, we have also covered a large class of nonstandard coding technology in the interest of completeness and potential future value. Very low bit rate video coding remains a challenging problem as does our understanding of the human visual system for perceptually optimum compression. The wide range of applications and bit rates, from video telephony at rates as low as 9.6 kbps to HDTV at 20 Mbps and higher, has acted as a catalyst for generating new ideas in tackling the different challenges characterized by the particular application. The area of image compression will remain an interesting and fruitful area of research as we focus on combining source coding with channel coding and multimedia networking.

Video communication requires sufficient resources to obtain the desired image quality. Availability of bandwidth and screen resolution from different devices on the client end, as well as compression techniques used for transmission also significantly influence to the video quality. Even though the H.264/AVC video compression standard and its scalable extension is known as Scalable Video Coding (SVC) was introduced and successfully addressed the challenge, some weaknesses persist especially for high-resolution video transmissions. The size of the bitstream generated by the SVC standard for ultra-high resolution video compression can only be met if transmitted on a network with a large bandwidth. The Scalable High-Efficiency Video Coding (SHVC) were introduced to overcome problems in the SVC standards. The new video compression standard is proven to reduce bandwidth requirements by up to 46.41 % compared to the SVC standards for the same video quality. This study introduces the performance analysis of SHVC standard video transmission as compared to SVC standard. Both compression standards are transmitted and emulated through network emulator called Mininet-Wifi to get the real transmission picture. The client-server video transmission scheme with different bandwidth and various client position are set in the experiment scenario. It is found that transmission time, packet loss, and video reconstruction quality on scalable video transmission with SHVC standard is better than scalable video transmissions with SVC standard.

In many video applications fractal video compression is use for video coding caused by its different features and lower bit rate. Self similarity concepts of image compression are used in fractal video compression. However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change. More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented. In video compression, finding the motion vectors (MV) is one of the major factor in motion estimation, due to its high computation complexity allows in between the frames. Many application like multimedia service contains the temporal type of redundancies for emission of video i.e. storage space, bandwidth and transmission cost to reduces this kind of redundancy the motion estimation is used while not degrade a quality of the video. There are number of algorithm has been evolved for fast block based matching techniques in motion estimation to remonstrate the drawbacks relate to diminishing the number of searching point, complexities and computational cost etc., by reason of its effortlessness the block-based technique is demand in motion estimation. Block matching algorithms attracts many researchers from algorithms.the different domain for motion vector estimation also for solving real life applications in motion estimation for video coding. This paper laborite a review of various fractal compression techniques and block matching motion estimation purpose. So, transmission of video takes more time to reach its destination. Therefore, some video compression techniques are involved to remove the redundancy that present in original video. In continuation of fractal image compression uses fractal video compression technique. One of the image compression methods is fractal coding [1]. Its clam is that within a given local region the correlation not only presents in adjacent pixels, but also in global regions or different regions. Mainly video compression technique contains two types of technique i.e. lossy and lossless compression [2]. In lossless technique, reconstruction of total original data is possible. Due to this characteristic, most lossless compression technique referred it for data and executable files etc. But few data may be removed permanently in lossy compression. Mainly two types of redundancies are evolving in sequence of video they are temporal redundancy & spatial redundancy. Spatial redundancies define as correlation present in a frame among neighboring pixel value. Temporal redundancy means by considering a redundancy present in between adjacent frames of images in video. The interframe coding concept uses to lower the temporal redundancy. Similarly, the intraframe coding concept lower the spatial type of redundancy.

The DCT-based transform coding technique was adopted by the international standards (ISO JPEG, ITU H.261/264/265, ISO MPEG-2/4/H, and many others) for nearly 30 years. Although researchers are still trying to improve its efficiency by fine-tuning its components and parameters, the basic structure has not changed in the past two decades.The deep learning technology recently developed may provide a new direction for constructing a high-compression image/video coding system. Recent results, particularly from the Challenge on Learned Image Compression (CLIC) at CVPR, indicate that this new type of schemes (often trained end-to-end) may have good potential for further improving compression efficiency.In the first part of this tutorial, we shall (1) summarize briefly the progress of this topic in the past 3 or so years, including an overview of CLIC results and JPEG AI Call-for-Evidence Challenge on Learning-based Image Coding (issued in early 2020). Because Deep Neural Network (DNN)-based image compression is a new area, several techniques and structures have been tested. The recently published autoencoder-based schemes can achieve similar PSNR to BPG (Better Portable Graphics, H.265 still image standard) and has superior subject quality (e.g., MSSSIM), especially at the very low bit rates. In the second part, we shall (2) address the detailed design concepts of image compression algorithms using the autoencoder structure. In the third part, we shall switch gears to (3) explore the emerging area of DNN-based video compression. Recent publications in this area have indicated that end-to-end trained video compression can achieve comparable or superior rate-distortion performance to HEVC/H.265. The CLIC at CVPR 2020 also created for the first time a new track dedicated to P-frame coding.

For low bit rate video compression, the quality of reconstructed video is usually poor. The high codec priority of region of interest (ROI) can improve image quality obviously. Nowadays, video segmentation methods are often used for extracting ROI, but these methods have high computational complexity and are not satisfied to real time communication. On the other hand, in most existing rate control algorithms, ROI can't select the low and high bit rate R-Q model adaptively. Aiming at these problems, in this paper, a simple and efficient approach of extracting ROI is proposed which can decrease the computational complexity of existing ROI extracting algorithms. Bits are distributed to ROI and non- ROI (NROI) respectively according to the image complexity and motion information. Moreover, the judgment criterion of distinguishing between low and high bit rate coding category is derived, which makes the algorithm select the R-Q model adaptively and decrease the rate control errors. In addition, the scheme of modifying the coding order of macro blocks (MBs) can enhance the objective image quality. Experiment results demonstrate that the proposed algorithm achieves a bit rate closer to the target, provides fewer skipped frames, and gets better objective and subjective image quality significantly compared with TMN7 and TMN8 algorithms.

Although JPEG technique is considered as the most popular image compression standard, it behaves high visual degradation at low bit rates. In this paper, an efficient DCT–based image compression technique is proposed to achieve high Compression Ratio (CR) with high quality at both high and low bit rates. This technique uses switching between JPEG compression technique at high bit rates and a novel Adaptive Lossy Image Compression (ALIC) technique at low bit rates. ALIC is proposed to overcome the drawbacks of JPEG technique at low bitrates. The performance of the proposed technique is analysed at low and high bit rates on both grey and colour images. Performances of both JPEG and ALIC techniques are analysed and compared. The experimental results reveal that the proposed ALIC technique achieves better CR with acceptable SNR in comparison with JPEG technique. Also, the resultant CR of ALIC technique can be considerably increased with a slight decrease of its PSNR. This decrease in PSNR does not result in a noticeable visual degradation of the compressed image. On the other hand, increasing the CR of JPEG technique results in a noticeable visual degradation due to the appearance of blocking effect in the reconstructed image. Thus, it is greatly recommended to use ALIC technique in the applications that require high CR with stable PSNR. ALIC is a general purpose technique that can be applied, not only on images, but also on any data source which uses Huffman coding to achieve better CR. Therefore, it is suitable for compression of text, image and video.

The problem of designing shared access packet-transport-based transmission systems for compressed video signals is studied. The feasibility of using conventional link-level and transport-level protocol services to transmit compressed video is examined by focusing on two practically important scenarios for compressed video transmission: (1) multipoint-to-multipoint video transmission using a 200 Mb/s implicit token passing (ITP) fiber-optic local area network (LAN); and (2) point-to-multipoint broadcast video distribution using a 90 Mb/s packet time-division multiplexing (packet-TDM) direct-broadcast satellite channel. To evaluate the performance of such shared-access broadband packet video systems accurate simulation models were developed that were driven by realistic 'broadcast quality' compressed video sources for the ITP-LAN and packet-TDM systems. The models were used to determine design tradeoffs between channel throughput, video quality (measured by clipping probability), and the transport-level and media-access-level protocol features and parameters implemented in the packet video network interface unit. >

To provide enhanced multimedia services for heterogeneous networks and terminal devices, Scalable Video Coding (SVC) has been developed to embed different quality of video in a single bitstream. Similar to classical compressed video transmission, different packets of a video bitstream have different impacts on received video quality. Therefore, distortion modeling and estimation are necessary in designing a robust video transmission strategy under various network conditions. In the paper, we present the first scheme of packet loss induced distortion modeling and estimation in SVC transmission. The proposed scheme is applicable to numerous video communication and networking scenarios in which accurate distortion information can be utilized to enhance the performance of video transmission. One major challenge in scalable video distortion estimation is due to the adoption of more complicated prediction structure in SVC, which makes the tracking of error propagation much more difficult than the non-scalable encoded video. In this research, we tackle such challenge by systematically tracking the propagation of errors under various prediction trajectories. Supplemental information about the compressed video is embedded into data packets to substantially simplify the modeling and estimation. Moreover, with supplemental data of inter prediction information, distortion estimation can be processed without parsing video bitstream which results in much lower computation and memory cost. With negligible effects on the data size, experimental results show that the proposed scheme is able to track and estimate the distortion with very high accuracy. This first ever scalable video transmission distortion modeling and estimation scheme can be deployed at either gateways or receivers because of its low computation and memory cost.

In recent years, real-time video communication over the internet has been widely utilized for applications like video conferencing. Streaming live video over heterogeneous IP networks, including wireless networks, requires video coding algorithms that can support various levels of quality in order to adapt to the network end-to-end bandwidth and transmitter/receiver resources. In this work, a scalable video coding and compression algorithm based on the Contourlet Transform is proposed. The algorithm allows for multiple levels of detail, without re-encoding the video frames, by just dropping the encoded information referring to higher resolution than needed. Compression is achieved by means of lossy and lossless methods, as well as variable bit rate encoding schemes. Furthermore, due to the transformation utilized, it does not suffer from blocking artifacts that occur with many widely adopted compression algorithms. Another highly advantageous characteristic of the algorithm is the suppression of noise induced by low-quality sensors usually encountered in web-cameras, due to the manipulation of the transform coefficients at the compression stage. The proposed algorithm is designed to introduce minimal coding delay, thus achieving real-time performance. Performance is enhanced by utilizing the vast computational capabilities of modern GPUs, providing satisfactory encoding and decoding times at relatively low cost. These characteristics make this method suitable for applications like video-conferencing that demand real-time performance, along with the highest visual quality possible for each user. Through the presented performance and quality evaluation of the algorithm, experimental results show that the proposed algorithm achieves better or comparable visual quality relative to other compression and encoding methods tested, while maintaining a satisfactory compression ratio. Especially at low bitrates, it provides more human-eye friendly images compared to algorithms utilizing block-based coding, like the MPEG family, as it introduces fuzziness and blurring instead of artificial block artifacts.

H.263+ is an emerging video compression standard for the low bit rate visual communication. Most H.263+ rate control algorithms, e.g. the one used in the test model of the near-term (TMN), focus on macroblock level bit allocation and low latency by assuming a constant frame rate video encoding sent through a constant bit rate (CBR) channel. These algorithms cannot handle the fluctuation of the channel bandwidth well so that the transmitted video quality is degraded seriously. In this work, we propose a new H.263+ rate control scheme which supports the variable bit rate (VBR) channel through the frame rate adjustment. In particular, a fast realization of encoding frame rate control based on motion information within a sliding window is developed to efficiently determine the tradeoff between spatial and temporal qualities. Experimental results are provided to demonstrate the superior performance of the proposed scheme.

In recent years, the demand for video transmission over wireless communication networks is growing very fast. The H.264 video compression standard which offers high quality at low bit rates, is a suitable codec for applications that require efficient video transmission over wireless networks. While the compressed videos are transmitted through error-prone networks, error robustness becomes an important issue. In this paper, a joint source-channel Lagrange optimization method in which the distortion of the decoder is estimated without using feedback which can be used for both multicast and point-to-point applications is proposed. The experimental results show that the new algorithm has a good performance in video transmission over error-prone channels by concealing the lost packets at the decoder.KeywordsWireless NetworksH.264Lagrange Rate-Distortion OptimizationSource-Channel coding

The past decade has witnessed the explosive growth of faces in video multimedia systems, e.g., videoconferencing and live shows. However, these videos are normally compressed at low bit-rates due to the bandwidth-hungry issue, leading to heavy quality degradation on face regions. This paper addresses the problem of face quality enhancement in compressed videos. Specifically, we establish a compressed face video (CFV) database, which includes 87,607 faces in 113 raw video sequences and their corresponding 904 compressed sequences. We find that the faces of compressed videos exhibit tremendous scale variation and quality fluctuation. Motivated by scalable video coding, we propose a multi-scale recurrent scalable network (MRS-Net) to enhance the quality of multi-scale faces in compressed videos. The MRS-Net is comprised by one base and two refined enhancement levels, corresponding to the quality enhancement of small-, medium- and large-scale faces, respectively. In the multi-level architecture of our MRS-Net, small-/medium-scale face quality enhancement serves as the basis for facilitating the quality enhancement of medium-/large-scale faces. Finally, experimental results show that our MRS-Net method is effective in enhancing the quality of multi-scale faces for compressed videos, significantly outperforming other state-of-the-art methods.

Low bit rate video coding has triggered intensive research in both academia and industry. To address the problem of heterogeneity, and meeting a very small bandwidth of mobile wireless networks, low bit rate scalable video compression is becoming more important. This paper discusses the current approach to wireless visual communications for the need of low bit rate scalable video compression, using discrete wavelet transform techniques. The discrete wavelet transform (DWT) has provided an efficient framework of multiresolution space-frequency representation with promising applications in image processing. This codec is based on 3D wavelet coding and adaptive quantization of the subbands coefficients. A common problem with some existing quantization methods is that the inherent image structures are severely distorted with course quantization. The Moving Picture Expert Group-4 (MPEG-4), an important component of the ITU standard is referred to as the benchmark of video standards for low bit rate video transmission.

Variable bit rate (VBR) compressed video targeted at constant video quality is also known to exhibit significant and multiple-time-scale rate variabilities. The burstiness of such a compressed VBR video complicates the management and provisioning of network resources for ever increasing multimedia services. In a heterogeneous internetworking environment, a single service provider typically doesn't control the entire path from multimedia streaming server to the client buffer. In this paper we analyze bit rate variabilities exhibited by scalable video coding (SVC) encoded VBR stream and present optical burst switching (OBS) network as a mechanism for VBR transport across the core network. In our experimental evaluation we use OBS inherent bursitification feature at the edge node and evaluate its effectiveness towards smoothing and transport of VBR video stream. SVC encoded VBR video is transported over OBS test bed and OBS burst assembly parameters like time threshold and offset time are tuned for a smooth transport of SVC encoded VBR video stream. Experimental results reveal that for a proper burst assembly time, OBS transported VBR video stream has low inter-frame time intervals as well as a high peak signal to noise ratio (PSNR).

Scalable High Efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard H.265/HEVC. Video rate control algorithm is out of the scope of video coding standards. Appropriate rate control algorithms are designed for various applications to overcome practical constraints such as bandwidth and buffering constraints. In most of the scalable video applications, such as video on demand (VoD) and broadcasting applications, encoded bitstreams with variable bit rates are preferred to bitstreams with constant bit rates. In variable bit rate (VBR) applications, the tolerable delay is relatively high. Therefore, we utilize a larger buffer to allow more variations in bitrate to provide smooth and high visual quality of output video. In this paper, we propose a fuzzy video rate controller appropriate for VBR applications of SHVC. A fuzzy controller is used for each layer of scalable video to minimize the fluctuation of QP at the frame level while the buffering constraint is obeyed for any number of layers received by a decoder. The proposed rate controller utilizes the well-known structural similarity index (SSIM) as a quality metric to increase the visual quality of the output video. The proposed rate control algorithm is implemented in HEVC reference software and comprehensive experiments are executed to tune the fuzzy controllers and also to evaluate the performance of the algorithm. Experimental results show a high performance for the proposed algorithm in terms of rate control, visual quality, and rate-distortion performance.