In this thesis, a high-efficient integrated pixel compensator architecture for the H.264/AVC standard has been proposed which can provide both inter and intra prediction functions for luma and chroma components of pixels. By decomposing the algorithms used for both prediction methods into small micro-operation steps, the fundamental arithmetic processing unit architecture capable for performing these operations can be first determined. Next, by considering the possible reference sample transfer issue, the overall compensator architecture will be built by using parallel processing units with some input and intermediate buffers which can be dynamically configured to perform proper computation schedules of different modes suitable for the nature input order of reference samples. The proposed design not only can avoid the additional data transposition buffer, but most importantly the data transfer time spent to fetch the reference samples can be overlapped with the data computation time. Since both arithmetic units and the intermediate data buffer for both inter and intra prediction processes have been shared, our integrated design can achieve more than 30% reduction of gate count compared with the sum of the separate designs. Our design can also lead to more than 38% saving of gate count compared with the previous designs. In addition to the data-path design, this thesis also addresses the memory bandwidth optimization issue which is especially important for the luma interpolation process. A new data-reuse buffer design based on a two-dimensional cache architecture to explore the possible data reuse among the inter and intra partitions will be proposed. The proposed design can be easily integrated with the H.264 interpolator to reduce the enormous demand of memory access. Our experimental results shows that our saving of memory bandwidth can be 20% more than what the best design can achieve by exploring the intra-partition data reuse only. Besides, our compensator can decode the videos up to HDTV resolution, and be applied for the dedicated H.264 hardware codec for various consumer devices.

This paper proposes new approaches to improve the coding performance of intra block coding in H.264/AVC via finite state machine and residual prediction. Grounding on high correlation between neighboring blocks, finite state machine is employed both at encoder and decoder to reduce the number of bits required for encoding to enhance coding performance. Two extra intra prediction modes are created in our proposed method. Through these two modes, the number of bits required to denote the current block is greatly reduced and low bit rate can be achieved. According to spatial correlation, intra-coded residual prediction reduces residual block by neighboring residual block. In this paper, we combine finite state machine with intra-coded residual prediction to achieve better coding performance. Experimental results show that the proposed method can greatly improve coding efficiency of intra macroblock coding in H.264/AVC.

This thesis dwells on two important problems in the field of video coding; namely rate control and spatial domain intra prediction. While the former is applicable generally to most video compression standards, the latter applies to recent advanced video compression standards such as H.264, VC1 and AVS. Rate control regulates the instantaneous video bit-rate to maximize a picture quality metric while satisfying channel rate and buffer size constraints. Rate control has an important bearing on the picture quality of encoded video. Typically, a quality metric such as Peak Signal-to-Noise ratio (PSNR) or weighted signal-to-noise ratio (WSNR) is chosen out of convenience. However neither metric is a true measure of perceived video quality. A few researchers have attempted to derive rate control algorithms with the combination of standard PSNR and ad-hoc perceptual metrics of video quality. The concept of using perceptual criterion for video coding was introduced in [7] within the context of perceptual adaptive quantization. In this work, quantization noise levels were adjusted such that more noise was allowed where it was less visible (busy and textured areas) while sensitive areas (typically flat and low detail regions) were finely quantized. Macro–blocks were classified into low detail, texture and edge areas depending on a classifier that studied the variance of sub-blocks within a macro-block (MB). The Rate models were trained from training sets of pre -classified video. One drawback of the above scheme as with standard PSNR was that neither accounts for the perceptual effect of motion. The work in [8] achieved this by assigning higher weights to the regions of the image that were experiencing the highest motion. Also, the center of the image and objects in the foreground are perceived as more important than the sides. However, attempts to use perceptual metrics for video quality have been limited by the accuracy of the video quality metrics chosen. In the recent years, new and improved metrics of subjective quality have been invented and their statistical accuracy has been studied in a formal manner. Particularly interesting is the work undertaken by ITU and the Video quality experts group (VQEG). VQEG conducted two phases of testing; in the first pha se, several algorithms were tested but they were not found to be very accurate, in fact none were found to be any more accurate than PSNR based metric. In the second phase of testing a few years later, a few new algorithms were experimented with, and it wa s concluded that four of these did achieve results good enough to warrant their standardization as a part of ITU –T Recommendation J.144. These experiments are referred to as the FR-TV (Full Reference Television) phase-II evaluations. ITU-T J.144 does not explicitly identify a single algorithm but provides guidelines on the selection of appropriate techniques to objectively measure subjective video quality. It describes four reference algorithms as well as PSNR. Amongst the four, the NTIA General Video Quality Model (VQM), [11] is the best performing and has been adopted by American National Standards Institute (ANSI) as a North American standard T1.801.03. NTIA’s approach has been to focus on defining parameters that model how humans perceive video quality. These parameters have been combined using linear models to produce estimates of video quality that closely approximate subjective test results. NTIA General Video Quality Model (VQM) has been proven to have strong correlation with subjective quality. In the first part of the thesis, we apply metrics motivated by NTIA-VQM model within a rate control algorithm to maximize perceptual video quality. We derive perceptual weights using key NTIA parameters to influence QP value used to decide degree of quantization. Our experiments demonstrate that a perceptual quality motivated standard TMN8 rate control in an H.263 encoder results in perceivable quality improvements over a baseline TMN8 rate control algorithm that uses a PSNR metric. Our experimental results on a set of 11 sequences show on an average reduction of 6% in bitrate using the proposed algorithm for the same perceptual quality as standard TMN-8. The second part of our thesis work deals with spatial domain intra prediction used in advance video coding standard such as H.264. The H.264 Advanced Video coding standard [36] has been shown to achieve video quality similar to older standards such as MPEG2 and H.263 at nearly half the bit-rate. Generally, this compression improvement is attributed to several new tools that were introduced in H.264 – including spatial intra prediction, adaptive block size for motion compensation, in-loop de-blocking filter, context adaptive binary arithmetic coding (CABAC), and multiple reference frames. While the new tools allow better coding efficiency, they also introduce additi onal computational complexity at both encoder and decoder ends. We are especially concerned here on the impact of Intra prediction on the computational complexity of the encoder. H.264 reference implementations such as JM [29] search through all allowed intra-rediction “modes” in order to find the optimal mode. While this approach yields the optimal prediction mode, it comes at an extremely heavy computational cost. Hence there is a lot of interest into well -motivated algorithms that reduce the computational complexity of the search for the best prediction mode, while retaining the quality advantages of full-search Intra4x4. We propose a novel algorithm to reduce the complexity of full search by exploiting our knowledge of the source statistics. Specifically, we analyze the transform domain energy distribution of the original 4x4 block in different directions and use the results of our analysis to eliminate unlikely modes and reduce the search space for the optimal I ntra mode. Experimental results show that the proposed algorithm achieves quality metrics (PSNR) similar to full search at nearly a third of the complexity. This thesis has four chapters and is organized as follows, in the first chapter we introduce basics of video encoding and subsequently present exiting work in the area of perceptual rate control and introduce TMN-8 rate control algorithm in brief. At the end we introduce spatial domain intra prediction. In the second chapter we explain the challenges present in combining NTIA perceptual parameters with TMN8 rate control algorithm. We examine perceptual features used by NTIA from a video compression perspective and explain how the perceptual metrics capture typical compression artifacts. We next present a two pass perceptual rate control (PRCII) algorithm. Finally, we list experimental results on set of video sequences showing on an average of 6% bit-rate reduction by using PRC-II rate control over standard TMN-8 rate control. Chapter 3 contains part-II of our thesis work on, spatial domain intra prediction . We start by reviewing existing work in intra prediction and then present the details of our proposed intra prediction algorithm and experimental results. We finally conclude this thesis in chapter 4 and discuss direction for the future work on both our proposed algorithms.

H.264/AVC intra prediction method is an efficient tool to reduce spatial redundancies by using multidirectional spatial prediction modes. In this paper, a novel intra prediction method is designed to improve coding efficiency. Firstly, we propose a direction-distance oriented prediction which considers the distance between the predict value and the reference samples according to the direction of the prediction modes. Secondly, we apply the concept of image restoration by using the projections onto convex sets (POCS) to intra prediction which uses adaptively filtering based on the surrounding reconstructed pixel to predict blocks. The experimental results show that the average bit-rate reduction of 0.75% and PSNR gain improved of 0.119dB are achieved.

Digital video is becoming extremely important nowadays and its importance has greatly increased in the last two decades. Due to the rapid development of information and communication technologies, the demand for Ultra-High Definition (UHD) video applications is becoming stronger. However, the most prevalent video compression standard H.264/AVC released in 2003 is inefficient when it comes to UHD videos. The increasing desire for superior compression efficiency to H.264/AVC leads to the standardization of High Efficiency Video Coding (HEVC). Compared with the H.264/AVC standard, HEVC offers a double compression ratio at the same level of video quality or substantial improvement of video quality at the same video bitrate. Yet, HE-VC/H.265 possesses superior compression efficiency, its complexity is several times more than H.264/AVC, impeding its high throughput implementation. Currently, most of the researchers have focused merely on algorithm level adaptations of HEVC/H.265 standard to reduce computational intensity without considering the hardware feasibility. What’s more, the exploration of efficient hardware architecture design is not exhaustive. Only a few research works have been conducted to explore efficient hardware architectures of HEVC/H.265 standard. In this dissertation, we investigate efficient algorithm adaptations and hardware architecture design of HEVC intra encoders. We also explore the deep learning approach in mode prediction. From the algorithm point of view, we propose three efficient hardware-oriented algorithm adaptations, including mode reduction, fast coding unit (CU) cost estimation, and group-based CABAC (context-adaptive binary arithmetic coding) rate estimation. Mode reduction aims to reduce mode candidates of each prediction unit (PU) in the rate-distortion optimization (RDO) process, which is both computation-intensive and time-consuming. Fast CU cost estimation is applied to reduce the complexity in rate-distortion (RD) calculation of each CU. Group-based CABAC rate estimation is proposed to parallelize syntax elements processing to greatly improve rate estimation throughput. From the hardware design perspective, a fully parallel hardware architecture of HEVC intra encoder is developed to sustain UHD video compression at 4K@30fps. The fully parallel architecture introduces four prediction engines (PE) and each PE performs the full cycle of mode prediction, transform, quantization, inverse quantization, inverse transform, reconstruction, rate-distortion estimation independently. PU blocks with different PU sizes will be processed by the different prediction engines (PE) simultaneously. Also, an efficient hardware implementation of a group-based CABAC rate estimator is incorporated into the proposed HEVC intra encoder for accurate and high-throughput rate estimation. To take advantage of the deep learning approach, we also propose a fully connected layer based neural network (FCLNN) mode preselection scheme to reduce the number of RDO modes of luma prediction blocks. All angular prediction modes are classified into 7 prediction groups. Each group contains 3-5 prediction modes that exhibit a similar prediction angle. A rough angle detection algorithm is designed to determine the prediction direction of the current block, then a small scale FCLNN is exploited to refine the mode prediction.

<p>In this thesis a H.264 baseline high definition encoder is implemented on CELL processor. The target video sequence is YUV420 1080p at 30 frames per second in our encoder. To meet real-time requirements, a system architecture which reduces DMA requests is designed for large memory accessing. Several key computing kernels: Intra frame encoding, motion estimation searching and entropy coding are designed and ported to CELL processor units. A main challenge is to find a good tradeoff between DMA latency and processing time. The limited 256K bytes on-chip memory of SPE has to be organized efficiently in SIMD way. CAVLC is performed in non-real-time on the PPE.</p><p> </p><p>The experimental results show that our encoder is able to encode I frame in high quality and encode common 1080p video sequences in real-time. With the using of five SPEs and 63KB executable code size, 20.72M cycles are needed to encode one P frame partitions for one SPE. The average PSNR of P frames increases a maximum of 1.52%. In the case of fast speed video sequence, 64x64 search range gets better frame qualities than 16x16 search range and increases only less than two times computing cycles of 16x16. Our results also demonstrate that more potential power of the CELL processor can be utilized in multimedia computing.</p><p> </p><p>The H.264 main profile will be implemented in future phases of this encoder project. Since the platform we use is IBM Full-System Simulator, DMA performance in a real CELL processor is an interesting issue. Real-time entropy coding is another challenge to CELL.</p>

In this thesis a H.264 baseline high definition encoder is implemented on CELL processor. The target video sequence is YUV420 1080p at 30 frames per second in our encoder. To meet real-time requirements, a system architecture which reduces DMA requests is designed for large memory accessing. Several key computing kernels: Intra frame encoding, motion estimation searching and entropy coding are designed and ported to CELL processor units. A main challenge is to find a good tradeoff between DMA latency and processing time. The limited 256K bytes on-chip memory of SPE has to be organized efficiently in SIMD way. CAVLC is performed in non-real-time on the PPE.   The experimental results show that our encoder is able to encode I frame in high quality and encode common 1080p video sequences in real-time. With the using of five SPEs and 63KB executable code size, 20.72M cycles are needed to encode one P frame partitions for one SPE. The average PSNR of P frames increases a maximum of 1.52%. In the case of fast speed video sequence, 64x64 search range gets better frame qualities than 16x16 search range and increases only less than two times computing cycles of 16x16. Our results also demonstrate that more potential power of the CELL processor can be utilized in multimedia computing.   The H.264 main profile will be implemented in future phases of this encoder project. Since the platform we use is IBM Full-System Simulator, DMA performance in a real CELL processor is an interesting issue. Real-time entropy coding is another challenge to CELL.

Intra-prediction is a method for coding standalone frames in video coding. Until now, this has mainly been done using linear formulae. Using an Artificial Neural Network (ANN) may improve the prediction accuracy, leading to improved coding efficiency. In this degree project, Fully Connected Networks (FCN) and Convolutional Neural Networks (CNN) were used for intra-prediction. Experiments were done on samples from different image sizes, block sizes, and block contents, and their effect on the results were compared and discussed. The results show that ANN methods have the potential to perform better or on par with the video coder High Efficiency Video Coding (HEVC) in the intra-prediction task. The proposed ANN designs perform better on smaller block sizes, but different designs could lead to better performance on larger block sizes. It was found that training one network for each HEVC mode and using the most suitable network to predict each block improved performance of the ANN approach. / Intra-prediktion är en metod för kodning av stillbilder i videokodning. Hittills har detta främst gjorts med hjälp av linjära formler. Användning av artificialla neuronnät (ANN) skulle kunna öka prediktionsnoggrannheten och ge högre effektivitet vid kodning. I detta examensarbete användes fully connected networks (FCN) och convolutional neural networks (CNN) för att utföra intra-prediktion. Experiment gjordes på prover från olika bildstorlekar, blockstorlekar och blockinnehåll, och de olika parametrarnas effekt på resultaten jämfördes och diskuterades. Resultaten visar att ANN-metoder har potential att prestera bättre eller lika bra som videokodaren High Efficiency Video Coding (HEVC) för intra-prediktion. De föreslagna ANN-designerna presterar bättre på mindre blockstorlekar, men andra ANN-designs skulle kunna ge bättre prestanda för större blockstorlekar. Det konstaterades att prestandan för ANN-metoderna kunde ökas genom att träna ett nätverk för varje HEVC-mode och använda det mest passande nätverket för varje block.