Recent broadcast standards support Layered Division Multiplexing (LDM) to achieve graceful degradation as signal quality degrades at the receiver. LDM is accomplished by using different constellations within the same Radio Frequency (RF) spectrum. LDM thus enables delivering multiple service tiers in a single broadcast channel. LDM when used in conjunction with scalable source coding codecs such as the Scalable extension of High Efficiency Video Coding (SHVC), further helps improve overall spectrum utilization and efficiency. In this paper we investigate a 2-tier broadcast LDM based service with one service tier aimed at lower video resolution such as 540p, 720p, 1080p for a mobile receiver (smaller/indoor antenna) and the other service tier targeting twice the video resolution of the lower tier, for stationary receivers (larger/outdoor antenna). The primary contribution of this paper is to identify 2-tier transmission configurations of interest to broadcasters and compare the spectrum and bitrate coding efficiency gains of an SHVC-based multi-tier service versus a simulcast (single layer) based multi-tier service for an Advanced Television Systems Committee (ATSC) 3.0 transmission system. Bitrate savings ranging from 38% and 57% is observed for the SHVC based layered system. For large coverage and pedestrian with a receiver test scenarios, channel utilization savings ranging from 23% to 46% is observed. For mobile and tablet in bedroom scenarios a smaller broadcast bandwidth savings ranging from 6% to 9% is observed.

Despite considerable progress in HDR image tone mapping for the past decade, little work has been done for HDR video. For applications such as film post-production, the capability of local tone manipulation is highly regarded by the content creators. This paper presents an interactive tone mapping scheme for HDR video sequences. It provides a simple scribble/stroke based interface for local tone manipulation and is capable of propagating user input information throughout a video sequence by using Gaussian mixture model (GMM) and edge preserving filtering. The experimental results demonstrated its effectiveness for HDR video tone mapping as well as its flexibility for users to easily and intuitively manipulate the appearance of the video while maintaining temporal consistency.

In 3D video (3DV) applications, a reduced number of views plus depth maps are transmitted or stored. When there is a need to render virtual views in between the actual views, the technique of depth image based rendering (DIBR) can be used to generate the intermediate views. To address the problem of noisy depth information in 3DV systems, we propose novel methods that can be easily incorporated into DIBR to improve synthesized image quality. These include: (1) a heuristic scheme with adaptive spatting that blends multiple warped reference pixels based on their depth, warped pixel positions and camera parameters; (2) an approximation of the first scheme with up-sampling for fast processing; (3) boundary only splatting; and (4) view weighting based on hole distribution. Experiment results show that the proposed methods can improve synthesis quality significantly.

In recent years, non-uniform LED based backlighting techniques have been used to build display prototypes and products with extremely high dynamic range, thus making possible to show video content with very high fidelity. These displays have two modulation channels: a low resolution LED layer that provides non-uniform backlighting, and a high resolution LCD front panel for color. Unfortunately, displaying an HDR image on such screens is not straightforward because the lower resolution of the LED layer and the cross-talk between LEDs make not possible to individually control the output of each pixel. Using the wrong backlighting results in low image quality and may even lead to visual artifacts such as false contouring and visible LED patterns. In this paper, we characterize the LED/LCD display using a general model and formulate the backlighting computation as an optimization problem. We further propose an iterative algorithm to solve the optimization problem. Compared to existing methods, our model is more precise and provides better image quality on the same display device.

Captions are text or logos superimposed on videos during a postproduction process. Caption detection in videos is useful for a variety of applications. For many applications, temporal consistency and stability is very important. Most of the prior work adopts certain post-processing procedures to smooth detected caption bounding boxes over time. Although these approaches mitigate the effect of the temporal inconsistency problem, they are unable to eliminate the problem. In this paper, we present a new caption detection algorithm that detects the 3D bounding boxes of caption regions in spatiotemporal volume space. 2D bounding boxes are then created by slicing the 3D bounding boxes. Since all the 2D bounding boxes corresponding to a caption area are sliced from one 3D bounding box, they are identical over time, thus ensuring temporal consistency of the result. The experiment results show that our new approach not only generates temporally consistent results but also results in higher detection accuracy.

A method is proposed for reducing the visibility of "contour artifacts," i.e., false contours resulting from color quantization in digital images. The method performs a multiscale analysis on the neighborhood of each pixel, determines the presence and scale of contour artifacts, and probabilistically dithers (perturbs) the color of the pixel. The overall effect is to "break down" the false contours, making them less visible. The proposed method may be used to reduce contour artifacts at the same bit depth as the input image or at higher bit depths. The contour artifact detection mechanism ensures that artifact-free regions remain unaffected during the process.

In this paper an efficient method of small object localization is proposed that integrates detection and tracking. The system is initialized using a strong detector and then it locates the object over time using a weak detector and a temporal tracker. Both of strong and weak detectors are based on foreground-background segmentation. The strong detector is created from shape analysis of foreground blobs and used to trigger the object tracker. The weak detector is built with outputs from the foreground detection likelihood and integrated into the trackerpsilas observation likelihood. In the particle filter-based object tracker, motion estimation is embedded to generate a better proposal distribution and a mixture model is tailored to handle the ambiguity of template matching due to cluttered background. As a case study, the proposed scheme is applied to ball detection and tracking in soccer game videos. Promising results are presented to illustrate the proposed method effectively handles heavy clutter, occlusion and motion blur.

High-quality digital video filtering applications pose interesting challenges. Noise reduction cannot come at the expense of losing picture detail or introducing post-filtering artifacts. We address these issues by proposing an adaptive spatio-temporal method for reducing noise in digital video. The novelty of this method is that it aims to achieve an "even" amount of noise reduction at each pixel while preserving picture detail. The emphasis on even noise reduction helps reduce post-filtering artifacts arising from uneven filtering. We further propose an algorithm for automatically estimating the key filtering parameters, given two intuitive high-level inputs, filtering strength and a tolerance value. The high-level inputs provide user-friendly "knobs" that could be adjusted to obtain the desired filtering performance.

A method is proposed for reducing the visibility of "banding artifacts," i.e. false contours resulting from color quantization in digital images. The method performs a multi-scale analysis on the neighborhood of each pixel, determines the presence and scale of banding artifacts, and probabilistically dithers (perturbs) the color of the pixel. The overall effect is to "break down" the false contours making them less visible. The proposed method may be used to reduce banding artifacts at the same bit depth as the input image or at higher bit depths. The banding detection mechanism ensures that artifact-free regions remain unaffected during the process.

Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on two key factors: how many particles are used and how these particles are re-located. In this paper, we estimate the number of required particles using the Kullback-Leibler distance (KLD), which is called KLD-sampling, and we use a hybrid dynamic model to generate diversified particles, which suits object's agile motion. Besides, we employ the mean shift analysis as a local mode seeking mechanism to make each particle more "informative". We demonstrate the performance of the proposed algorithm tracking the ball in sports video clips.

Video denoising is an important feature of many modem video encoding architectures since it can considerably enhance coding efficiency while at the same time improving objective and subjective quality. In this paper, a new scheme for video denoising based on spatio-temporal filtering is presented, which can be used as a preprocessing stage within a video encoder. Spatial filtering is performing through the adaptive selection and combination of a wavelet based and a 2D Wiener filter, while for temporal filtering we employ bidirectional block based motion compensation using the Enhanced Predictive Zonal Search (EPZS) algorithm. Experimental results are presented which show a significant improvement in video quality when the denoised video sequence is encoded with an H.264 encoder.