Multicore-based 3D-DWT video encoder

Vicente Galiano,Manuel P Malumbres,Otoniel López-Granado,Héctor Migallón

doi:10.1186/1687-6180-2013-84

Vicente Galiano, Manuel P Malumbres + Show 2 more

Open Access

https://doi.org/10.1186/1687-6180-2013-84

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Abstract

Abstract Three-dimensional wavelet transform (3D-DWT) encoders are good candidates for applications like professional video editing, video surveillance, multi-spectral satellite imaging, etc. where a frame must be reconstructed as quickly as possible. In this paper, we present a new 3D-DWT video encoder based on a fast run-length coding engine. Furthermore, we present several multicore optimizations to speed-up the 3D-DWT computation. An exhaustive evaluation of the proposed encoder (3D-GOP-RL) has been performed, and we have compared the evaluation results with other video encoders in terms of rate/distortion (R/D), coding/decoding delay, and memory consumption. Results show that the proposed encoder obtains good R/D results for high-resolution video sequences with nearly in-place computation using only the memory needed to store a group of pictures. After applying the multicore optimization strategies over the 3D DWT, the proposed encoder is able to compress a full high-definition video sequence in real-time.

Highlights

Most of the popular video compression technologies operate in both intra and inter coding modes
6 Conclusions In this paper, we have presented the 3D-group of pictures (GOP)-RL, a fast video encoder based on 3D wavelet transform and efficient run-length coding
We have compared our algorithm against 3D-set partitioning in hierarchical trees (SPIHT), H.264, ×264, H.263, MPEG-2, and MPEG-4 encoders in terms of R/D, coding delay, and memory requirements

Summary

Introduction

Most of the popular video compression technologies operate in both intra and inter coding modes. We present an in-depth analysis of the use of multicore strategies to accelerate the 3D-DWT Using these strategies, the proposed encoder is able to compress a full high-definition (HD) video sequence in real-time. 2.1 Fast run-length coding In the proposed encoder, the quantization process is performed by two strategies: one coarser and another finer. For each coefficient in that buffer, if it is not significant, a run-length count of insignificant symbols at this level is increased (run lengthL). In order to encode the count of insignificant symbols, we use a RUN symbol After encoding this symbol, the run-length count (run lengthL) is stored in a similar way as in the case of significant coefficients. The formal description of the depicted algorithm can be found in Algorithm 1

2: Scan Buffer in horizontal raster order

Findings

Conclusions