CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image - a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page

Methodology and optimizing of multiple frame format buffering within FPGA H.264/AVC decoder with FRExt.

Digital representation of video data is an inherently resource demanding problem that continues to necessitate the development and refinement of coding methods. The H.264/AVC standard, along with its recent Fidelity Range Extensions amendment (FRExt), is quickly being adopted as the standard codec for broadcast and distribution of high definition video. The FRExt amendment, while not necessarily affecting the overall decoder architecture, presents an added complexity of providing efficient memory management for buffering intermediate frames of various pixel color samplings and depths. This thesis evaluated the role of designing the frame buffer of a hardware video decoder, with integrated support for the H.264/AVC codec plus FRExt. With focus on organizing external memory data access, the frame buffer was designed to provide intermediate data storage for the decoder, while using an efficient store and load scheme that takes into consideration each frame pixel format of the video data. VHDL was used to model the frame buffer. Exploitation of reconfigurability and post-synthesis FPGA simulations were used to evaluate behavior, scalability and power consumption, while providing an analysis of approaches to adding FRExt to the memory management. Real-time buffer performance was achieved for two common frame formats at 1080 HD resolution; and an innovative pipeline design provides dynamic switching of formats between video sequences. As an additional consequence of verifying the model, a preexisting Baseline H.264/AVC decoder testbench was augmented to support testing of multiple frame formats