The demand for streaming video content is on the rise and growing exponentially. Networks bandwidth is very costly and therefore there is a constant effort to improve video compression rates and enable the sending of reduced data volumes while retaining quality of experience (QoE). One basic feature that utilizes the spatial correlation of pixels for video compression is Intra-Prediction, which determines the codec’s compression efficiency. Intra prediction enables significant reduction of the Intra-Frame (I frame) size and, therefore, contributes to efficient exploitation of bandwidth. In this presentation, we propose new Intra-Prediction algorithms that improve the AV1 prediction model and provide better compression ratios. Two (2) types of methods are considered: )1( New scanning order method that maximizes spatial correlation in order to reduce prediction error; and )2( New Intra-Prediction modes implementation in AVI. Modern video coding standards, including AVI codec, utilize fixed scan orders in processing blocks during intra coding. The fixed scan orders typically result in residual blocks with high prediction error mainly in blocks with edges. This means that the fixed scan orders cannot fully exploit the content-adaptive spatial correlations between adjacent blocks, thus the bitrate after compression tends to be large. To reduce the bitrate induced by inaccurate intra prediction, the proposed approach adaptively chooses the scanning order of blocks according to criteria of firstly predicting blocks with maximum number of surrounding, already Inter-Predicted blocks. Using the modified scanning order method and the new modes has reduced the MSE by up to five (5) times when compared to conventional TM mode / Raster scan and up to two (2) times when compared to conventional CALIC mode / Raster scan, depending on the image characteristics (which determines the percentage of blocks predicted with Inter-Prediction, which in turn impacts the efficiency of the new scanning method). For the same cases, the PSNR was shown to improve by up to 7.4dB and up to 4 dB, respectively. The new modes have yielded 5% improvement in BD-Rate over traditionally used modes, when run on K-Frame, which is expected to yield ~1% of overall improvement.
The demand for high quality video is permanently on the rise and with it the need for more effective compression.
Compression scope can be further expanded due to increased spatial correlation of pixels within a high quality video frame.
One basic feature that takes advantage of pixels’ spatial correlation for video compression is Intra-Prediction, which
determines the codec’s compression efficiency. Intra-Prediction enables significant reduction of the Intra-frame (I-frame)
size and, therefore, contributes to more efficient bandwidth exploitation. It has been observed that the intra frame coding
efficiency of VP9 is not as good as that of H.265/MPEG-HEVC. One possible reason is that HEVC’s Intra-prediction
algorithm uses as many as 35 prediction directions, while VP9 uses only 9 directions including the TM prediction mode.
Therefore, there is high motivation to improve the Intra-Prediction scheme with new, original and proprietary algorithms
that will enhance the overall performance of Google’s future codec and bring its performance closer to that of HEVC. In
this work, instead of using different angles for predictions, we introduce four unconventional Intra-Prediction modes for
the VP10 codec – Weighted CALIC (WCALIC), Intra-Prediction using System of Linear Equations (ISLE), Prediction of
Discrete Cosine Transformations (PrDCT) Coefficients and Reverse Least Power of Three (RLPT). Employed on a
selection eleven (11) typical images with a variety of spatial characteristics, by using Mean Square Error (MSE) evaluation
criteria, we show that our proposed algorithms (modes) were preferred and thus selected around 57% of the blocks,
resulting in a reduced average prediction error, i.e. the MSE of 26%. We believe that our proposed techniques will achieve
higher compression without compromising video quality, thus improving the Rate-Distortion (RD) performances of the
compressed video stream.
During the last twenty years, digital imagers have spread into industrial and everyday devices, such as satellites, security cameras, cell phones, laptops and more. “Hot pixels” are the main defects in remote digital cameras. In this paper we prove an improvement of existing restoration methods that use (solely or as an auxiliary tool) some average of the surrounding single pixel, such as the method of the Chapman-Koren study 1,2. The proposed method uses the CALIC algorithm and adapts it to a full use of the surrounding pixels.