Rate Control (RC) plays an important role in video encoding. Traditional solutions are using fixed rate or fixed quantization parameters as the unified rate-control targets for all videos in one given video application. However, unified rate-control targets tend to have some bad encoding cases because of applying wrong rate for the video content. In this paper, we propose one content-adaptive rate control solution. We employ one neural-network based model which can end-to-end learn the optimal rate-control target appropriate to the content characteristics. The experimental results show that the proposed model can predict the optimal rate-factor value with the accuracy up to 77.637%. With this model, the proposed video-encoding method can significantly decrease the encoding quality fluctuation.

Modern digital family technology enables people surf the Internet and watch videos via a large screen. This paper proposes an effective scheme for using hand gestures rather than the common remote controllers to browse the web pages on a large TV screen. The proposed scheme models four gesture modes: mouse mode, scroll mode, zoom mode and input mode to help the user browse web pages naturally and comfortably. Then we combine RGB, depth, motion information and face detection to achieve accurate and real-time hand segmentation and gesture recognition for enabling the four gesture modes. The experiments show the proposed scheme works well in various illumination environments and complicated backgrounds with multiple moving humans. The recognition accuracy of hand shapes in the proposed scheme arrives at 98.50%, and the successful rate for visual digits input reaches 89.00%. Furthermore, the frame rate of the hand-gesture detection and recognition is about 18 fps. Thus the scheme is accurate, real-time and natural.

Interactive screen sharing requires extremely low latency end-to-end transmission, which in turn requires highly efficient and low-complexity screen compression. In this paper, we present a block-based low-complexity screen compression scheme, in which multiple block modes are adopted to exploit the intra- and inter-frame redundancies. In particular, we classify the intra-coded blocks to pictorial blocks and textual blocks using a proposed fast block classification algorithm, which exploits the discriminative features between the pictorial and the textual blocks. Then, we design a low-complexity, yet efficient, algorithm to compress the textual blocks. We use base colors and escape colors to represent and quantize the textual pixels, which not only achieves high compression ratios but also preserves a high quality on textual pixels. The two-dimensionally predictive index coding and hierarchical pattern coding technologies are used to exploit local spatial correlations and global pattern correlation, respectively. To further utilize the correlation between the luminance and chrominance channels, we propose a joint-channel index coding method. We compare the coding efficiency and the computational complexity of the proposed scheme against the standard image coding schemes such as JPEG, JPEG2000, and PNG, the compound image compressor HJPC, and the popular video coding standard H.264. We also compare the visual quality of the proposed scheme against H.264 intra coding, JPEG2000, and HJPC. The evaluation results show that the proposed scheme achieves superior or comparable compression efficiency with much lower complexity than other schemes in most of the cases.

Screen is becoming a new dimension in cloud computing platforms, and low latency screen sharing in unreliable networks is becoming more and more important. Due to the different characteristics between the screen codecs and video codecs, current transmission technologies on low-latency video streaming cannot be directly applied to screen sharing. So in this paper we first theoretically analyze the difference in latency performance between ARQ and FEC for the UDP-based screen sharing. Then, considering the characteristics of the main-stream screen codecs, we propose an improved ARQ scheme to decrease the transmission latency. The experimental results show that the proposed system achieves better latency performance than the popular systems.

This paper presents a block-based low-complexity screen compression scheme. In this scheme, the input screen is split into non-overlapping blocks which are classified as pictorial blocks and textual blocks. We design a low-complexity yet efficient algorithm to compress the textual blocks. We use base colors plus escape pixels to represent and quantize the text pixels, and such quantization mechanism not only achieves high compression efficiency but also keeps low encoding/decoding complexity. We also propose the two-direction predictive index coding and hierarchical pattern coding technologies to utilize the local spatial correlation and the global pattern correlation for text pixels. In addition, to utilize the correlation between the luminance and chrominance channels, we propose a joint-channel index coding method to further improve the compression efficiency. The compression efficiency and complexity of the proposed method is evaluated against the popular image codecs JPEG/JPEG200 and PNG, a recent published screen compression scheme HJPC as well as the popular video codec H.264.

In this paper, we present a browser-friendly hybrid JPEG/PNG codec for compound images. First we employ a simple yet efficient block classification algorithm to identify the blocks to pictorial and textural ones. And then JPEG is used to encode the pictorial blocks and PNG is used for textual blocks. Our evaluation results show that our codec significantly outperforms JPEG and PNG in terms of rate-distortion performance, and also it outperforms JPEG, JPEG2000 and DjVu in terms of visual quality. Moreover, since JPEG and PNG are naturally supported by modern browsers, the coded images generated from our proposed coder can be natively supported by browsers and possible to be widely deployed in Web applications.

In this paper, we propose an efficient encoding solution for the screen-sharing applications with multiple clients connected. We first present a lightweight screen codec to compress the complicated screen content. Based on the architecture of the proposed codec, we propose a one-pass encoding algorithm for multiple bit-rates. The one-pass encoding algorithm enables the host to only involve in one-pass encoding process for multiple bitrates, and as a result the computation complexity of the host is decreased significantly. Specially, in the limited computing-resource case, the one-pass encoding algorithm can improve the screen-sharing performance by about 43%, in terms of framerates the clients can get. In addition, based on our codec, we propose a fast screen transcoding scheme for the data-center based screen-sharing applications.

In this paper, we present a proxy-based mobile web browser with rich experiences. We use the server-side web parsing and rendering to leverage the browser computing logic. We use a composite screen format to represent the display of the web content, incorporating the web background screen and the dynamic web objects. And then we employ a slice-based screen encoding scheme to efficiently compress the web background screen. Besides the display screen of the web content, we also send the side information of the web objects to enable the designed object-level interaction mechanisms. The experimental results show that our browser can achieve the superior browsing speed, compared with the native browser and yield much better visual quality than the existing proxy-based browser

Since variable block-size motion compensation (MC) and rate-distortion optimization (RDO) techniques are adopted in H.264/MPEG-4 AVC, modes and motion vectors (MVs) in input stream can no longer be reused equivalently efficient over a wide range of bit rate in transcoded streams. This paper proposes a new RDO model to maintain good coding efficiency and greatly reduce computation of the H.264/MPEG-4 AVC transcoding, in which the distortion caused by motion and mode changes is not calculated directly from the sum of absolute difference (SAD) or the sum of square difference (SSD) between source signals and interpolated prediction signals. Instead, distortion is directly estimated from MV variation and the power spectrum (PS) of the prediction signal generated from input stream. The proposed RDO model can be applied to both the pixel-domain transcoding and the transform-domain transcoding even when coded signals are not reconstructed at all. Furthermore, the techniques as to derive the Lagrangian multiplier in the proposed model are developed in respective pixel- and transform-domains. Additionally, we propose an H.264/MPEG-4 transcoding scheme that demonstrates the advantage of the proposed RDO model in terms of peak signal-to-noise ratio and transcoding speed, in which P-pictures are transcoded in the pixel domain for achieving reconstructed high quality and B-pictures are transcoded in the transform domain for high-transcoding speed.

This paper presents a transcoder which transcodes to FGS streams from H.264/AVC hierarchical B-pictures. First, the DCT-domain architecture is designed for fast FGS transcoding. Then, we propose a mode decision method in DCT domain to achieve a trade-off between the performances at low bit-rate and high bit-rate. Experimental results demonstrated that our method can improve the coding performance up to 1 dB at low rate and only lose at worst 0.5 dB at high rate.

This paper focuses on the mode decision and motion selection problem when H.264/AVC video streams are transcoded in spatial resolution. A fast downsizing transcoding scheme is developed in which a new rate-distortion (R-D) optimal mode decision mechanism is presented for high speed transcoding as well as high coding efficiency. A model for estimating relative prediction errors is applied in this paper, which is free from computation of interpolation and SAD/SSD computation. Based on the selected model, a motion refinement within a distance of 1 pixel is performed after mode decision. Experimental results demonstrate that our method can significantly speed up the spatial resolution reduction process, while achieving high coding efficiency

An efficient rate-distortion (R-D) optimal method for transcoding hierarchical B-pictures is proposed in this paper. A new R-D model is presented for fast transcoding hierarchical B-pictures in DCT domain, in which the total R-D optimization problem is adjusted to motion R-D optimization and texture R-D optimization separately. Accordingly, a mechanism for fast mode decision is proposed to enable the mode and motion adjustment in hierarchical B-picture DCT-domain transcoding. Experimental results show that the proposed transcoding scheme with the new R-D optimization can achieve 4-dB average PNSR improvement at low bit-rate, and similar performance at high bit-rate, compared to the transcoding by reusing the input motion information. Moreover, as the transcoding is performed in DCT domain, it is fast and simple enough for many real applications.