Deblocking filtering in the H.264/AVC standard is a computationally complex process because of the filter's high content adaptivity. Furthermore, the deblocking filter introduces a significant number of data dependencies, making parallel processing not obvious. Our previous works analyzed the dependencies of the filter and proposed a massively-parallel implementation, specifically tailored for execution on a single GPU. In this paper, we extend this work by proposing a parallel processing scheme for accelerating deblocking filtering using multiple CPU cores or GPUs. This scheme allows for standard-compliant filtering, regardless of slice configuration. Results show that our multi-GPU implementation using our proposed scheme achieves faster-than real-time deblocking at over 3794 frames per second for 1080p video pictures by using three GPUs. A multi-core CPU implementation using 8 CPU cores allows 1080p deblocking filtering of up to 695 frames per second.

Many applications require real-time decoding of high-resolution video pictures, for example, quick editing of video sequences in video editing applications. To increase decoding speed, parallelism can be exploited, yet, block-based image and video coding standards are difficult to decode in parallel because of the high number of dependencies between blocks. This paper investigates the parallel decoding capabilities of the new JPEG XR image coding standard for use on the massively-parallel architecture of the GPU. The potential of parallelism of the hierarchical frequency coding scheme used in the standard is addressed and a parallel decoding scheme is described suitable for real-time decoding of Ultra High Definition (4320p) Motion JPEG XR video sequences. Our results show a decoding speed of up to 46 frames per second for Ultra High Definition (4320p) sequences with high-dynamic range (32-bit/4:2:0) luma and chroma components.

The deblocking filter in the MPEG-4 AVC/H.264 standard is computationally complex because of its high content adaptivity, resulting in a significant number of data dependencies. These data dependencies interfere with parallel filtering of multiple macroblocks (MBs) on massively parallel architectures. In this letter, we introduce a novel MB partitioning scheme for concurrent deblocking in the MPEG-4 AVC/H.264 standard, based on our idea of deblocking filter independency, a corrected version of the limited error propagation effect proposed in the letter. Our proposed scheme enables concurrent MB deblocking of luma samples with limited synchronization effort, independently of slice configuration, and is compliant with the MPEG-4 H.264/AVC standard. We implemented the method on the massively parallel architecture of the graphics processing unit (GPU). Experimental results show that our GPU implementation achieves faster-than real-time deblocking at 1309 frames per second for 1080p video pictures. Both software-based deblocking filters and state-of-the-art GPU-enabled algorithms are outperformed in terms of speed by factors up to 10.2 and 19.5, respectively, for 1080p video pictures.

Although pixel shaders were designed for the creation of programmable rendering effects, they can also be used as generic processing units for vector data. In this paper, attention is paid to an implementation of the YCoCg-R to RGB color space transform, as defined in the H.264/AVC Fidelity Range Extensions, by making use of pixel shaders. Our results show that a significant speedup can be achieved by relying on the processing power of the GPU, relative to the CPU. To be more specific, high definition video (1080p), represented in the YCoCg-R color space, could be decoded to RGB at 30 Hz on a PC with an AMD Athlon XP 2800+ CPU, an AGP bus and an NVIDIA GeForce 6800 graphics card, an effort that could not be realized in real-time by the CPU.