An encoding device includes a color component separating unit for separating an input bit stream for the respective color components, a block dividing unit for dividing an input color component signal into blocks to generate a signal of an encoding unit area, a predicted image generating unit for generating a predicted image for the signal, a determining unit for determining a prediction mode used for encoding according to a prediction efficiency of the predicted image, a prediction error encoding unit for encoding a difference between the predicted image corresponding to the prediction mode determined by the determining unit and the input color component signal, and an encoding unit for variable length-coding the prediction mode, an output from the prediction error encoding unit, and a color component identification flag indicating the color component to which the input bit stream belongs as a result of the color component separation.

Image encoding device, image decoding device, image encoding method, and image decoding method

High Efficiency Video Coding (HEVC) adopts the quadtree structured coding unit (CU), which allows recursive splitting into four equally sized blocks. At each depth level, it enables SKIP mode, Merge mode, Inter 2N×2N, Inter 2N×N, Inter N×2N, Inter 2N×nU, Inter 2N×nD, Inter nL×2N, Inter nR×2N, Inter N×N (only available for the smallest CU), Intra 2N×2N and Intra N×N (only available for the smallest CU) in inter frames. Similar to H.264/AVC, the mode decision process in HEVC is performed using all the possible depth levels (or CU sizes) and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency, but leads to a very high computational complexity. Since the optimal prediction mode is highly content-dependent, it is not efficient to use all the modes. In this paper, we propose a fast inter mode decision algorithm for HEVC by jointly utilizing the inter-level correlation of quadtree structure and the spatio-temporal correlation. There exist strong correlations of the prediction mode, the motion vector and RD cost between different depth levels and between spatially-temporally adjacent CUs. We statistically analyze the prediction mode distribution at each depth level and the coding information correlation among adjacent CUs. Based on the analysis results, three adaptive inter-mode decision strategies are proposed including early SKIP mode decision, prediction size correlation based mode decision and RD cost correlation based mode decision. Experimental results show that the proposed overall algorithm can save 49%-52% computational complexity on average with negligible loss of coding efficiency, exhibiting applicability to various types of video sequences.

Adaptive Inter-Mode Decision for HEVC Jointly Utilizing Inter-Level and Spatiotemporal Correlations

The ITU-T draft H.264 video coding standard has been designed enabling a new generation of digital video applications.While the basic coding framework of the standard is similar to that of currently popular video standard, H.264 includes many coding features not present in earlier standard. We firstly introduces the important features and key techniques in H.264. Then, we presents the performance of it. Study on the new generation ITU-T draft H.264 is of great importance in application field.

Overview of the H.264 / AVC Video Coding Standard

An apparatus configured to code video information comprises a memory unit and a processor in communication with the memory unit. The memory unit is configured to store video information associated with a reference layer (RL) and an enhancement layer (EL). The EL comprises an EL video unit and the RL comprises an RL video unit corresponding to the EL video unit. The processor is configured to perform upsampling and bit-depth conversion on pixel information of the RL video unit in a single combined process to determine predicted pixel information of the EL video unit, and determine the EL video unit using the predicted pixel information.

Device and method for scalable coding of video information

Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a lightweight and real-time traffic light detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic lights, and a lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic light detection module can achieve $ ± 1 . 5 m errors at stop lines when working with other self-driving modules.

Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles

Traffic Light Detection(TLD) and understanding their state semantics at intersections plays a pivotal role in driver assistance systems and, by extension, autonomous vehicles. Despite of several reliable traffic light state detection approaches in literature, traffic light state recognition still remains an open problem due to outdoor perception challenge which includes occlusions, illumination and scale variations. This paper presents a vision-based traffic light structure detection and convolutional neural network (CNN) based state recognition method, which is robust under different illumination and weather conditions. In the first step, traffic light candidate regions are generated by performing HSV based color segmentation, which are then filtered out using shape and area analysis. Further, in order to incorporate the structural information of traffic light in diverse background scenarios, Maximally Stable Extremal Region (MSER) approach is employed, which helps to localize the correct traffic light structure in the image. To further validate the traffic light candidate regions, Histogram of Oriented Gradients (HOG) features are extracted for each region and traffic light structures are validated using Support Vector Machine (SVM). The state of the traffic lights are then recognized using CNN. To evaluate the performance of the proposed method, we present several results under a variety of lighting conditions in a real-world environment. Experimental result shows that the proposed method outperforms other vision based conventional methods under varying light and weather conditions.

An efficient vision-based traffic light detection and state recognition for autonomous vehicles

Scalable video coding (SVC) is currently developed as an extension of H.264/AVC. In the SVC encoder, an exhaustive search technique is employed to select the best coding mode for each macro block (MB). This technique achieves an optimal trade-off between rate and distortion, but it requires an extremely large encoding time. In this paper, we propose a fast mode decision algorithm for inter-layer coding at the enhancement layer. The proposed algorithm predicts the mode of each MB at the enhancement layer using the modes of a co-located MB and its neighboring MBs at the base layer. The proposed method can achieve a time saving of up to 74% in spatial scalability and 63% in coarse grain quality scalability with negligible loss of quality and bit rate increment.

Fast mode decision algorithm for inter-layer coding in scalable video coding

In this paper, we propose a novel method to automatically configure a pan-tilt-zoom camera network in order to maximize coverage and visual quality in complex indoor environments. Based on a suitable modeling of cameras and environment, the optimization procedure determines the most appropriate camera position and settings to fulfill a given coverage objective. To achieve this goal, we use a particle swarm optimizer with an appropriate fitness function that takes into account a number of concurrent metrics and constraints. Furthermore, the solution is found working into a simulated environment obtained with the ray-tracing software. The proposed solution has been tested in various synthetic and real environments, taking into account the presence of obstacles and other constraints. We also simulated dynamic situations, such as cameras failures, to test the capability of fast camera network reconfiguration. For the performance evaluation, and in addition to the simulation in virtual scenes, we have also conducted a validation phase in real-world scenarios, where we assessed how the introduction on the optimal configuration improves the solution of some typical computer vision tasks.

Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment

In this paper we present a novel method for video cameras positioning and reconfiguration, to maximize visual coverage in complex indoor environments. Based on a suitable modeling of the camera field-of-view and of the environmental setup, the optimization procedure determines the most appropriate configuration of cameras to satisfy a coverage objective, taking into account a number of parameters on the quality of view at the camera position. This includes the global ground area coverage, the expected geometric distortion, and the entropy of the image. The proposed solution has been validated in different environmental setups, including synthetic settings, taking into account the presence of obstacles and constraints.

Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization

Scalable video coding (SVC) has found more and more applications in a variety of the adaptive network streaming and the terminal In the current SVC, an exhaustive search technique is employed to select the best coding mode for each macro block This technique achieves the optimal trade-off between the bit-rate and distortion, but it results in extremely large encoding time which obstructs it from practical use In this paper, we propose a fast mode decision algorithm for inter frame coding for spatial and coarse grain signal to noise ratio (SNR) scalability In the proposed method, we predict the enhancement layer mode using both collocated macro block and its neighboring block mode in the base layer The proposed method can achieve the time saving up to 78 percent in spatial scalability and 35 percent in coarse grain quality scalability (CGS) with the negligible loss of quality and increment in bit rate

Krishna Reddy Konda

Papers

An efficient vision-based traffic light detection and state recognition for autonomous vehicles

Fast mode decision algorithm for inter-layer coding in scalable video coding

Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment

Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization

Fast mode decision algorithm for spatial and SNR scalable video coding