视频内容编码对比分析

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"这篇文档是Jeremiah Golston在2004年嵌入式系统大会上的演讲，主题是关于视频内容的媒体编码器比较。它探讨了数字视频在各种应用中的普及，包括视频电话、安全监控、DVD、数字电视、互联网视频流、数码摄录机、手机媒体和个人视频录像机等。视频压缩对于这些应用至关重要，因此有多种行业标准和专有算法的视频编解码器被用于实现视频的数字存储和传输。随着算法的进步和低成本集成电路（如数字媒体处理器）处理能力的提升，压缩标准也在不断发展。文章将分析不同压缩标准之间的差异以及针对目标应用优化的实现策略。" 本文的核心知识点包括： 1. **视频编码器的重要性**：视频编码器是数字视频技术的基础，它们通过压缩视频数据，使得视频能在有限的存储空间和带宽条件下进行存储和传输。 2. **应用场景**：从视频电话到个人视频录像机，数字视频在多个领域得到广泛应用，这都离不开有效的视频压缩技术。 3. **行业标准与专有算法**：存在多种视频编解码器标准，如MPEG系列（MPEG-1、MPEG-2、MPEG-4）、H.26x系列（H.264/AVC、H.265/HEVC）、VP9、AV1等，以及各公司开发的专有算法，它们各有优缺点，适用于不同的场景。 4. **压缩标准的演进**：随着技术进步，压缩标准不断升级，如H.265相比H.264能提供更高的压缩效率，而AV1则旨在提供开源且无版权费的高效率编码。 5. **处理能力的提升**：低功耗、高性能的集成电路，如数字媒体处理器，为视频编码提供了硬件支持，推动了压缩技术的发展。 6. **标准与实现的差异**：即使基于同一标准，不同的优化策略也可能导致编码效果和性能的差异。例如，针对实时通信和高质量存储可能需要不同的优化重点。 7. **目标应用优化**：每个标准的实现都会根据其主要应用的需求进行优化，比如，移动设备可能更关注低延迟和低功耗，而流媒体服务可能更关注带宽效率。 8. **评估指标**：评价视频编码器性能的关键因素包括压缩效率（数据率与图像质量的关系）、编码速度、解码复杂性、延迟、版权问题以及跨平台兼容性。这篇论文深入探讨了不同媒体编码器如何适应和优化以满足各类视频应用的需求，并对如何选择和使用合适的编码标准提供了有价值的见解。

The main functions in the JPEG standard shown in Figure 2 formed the core for all of the major compression

algorithms that followed. Key functions include the following:

Block-based Processing: Dividing each frame into blocks of pixels so that processing of the image or video

frame can be conducted at the block level.

Intra-frame Coding: Exploiting the spatial redundancies that exist within the image or video frame by

coding the original blocks through transform, quantization, and entropy coding. The frame is coded based on

spatial redundancy only. There is no dependence on surrounding frames.

8x8 DCT: Each 8x8 block of pixel values is mapped to the frequency domain producing 64 frequency

components

Perceptual Quantization: Scale the bit allocation for different frequencies typically generating many zero

valued coefficients.

Run-length Coding: Represent the quantized frequency coefficients as a non-zero coefficient level followed

by runs of zero coefficients and a final end of block code after the last non-zero value.

Variable Length (Huffman) Coding: Huffman coding converts the run-level pairs into variable length

codes (VLCs) with the bit-length optimized for the typical probability distribution.

JPEG has extensions for lossless and progressive coding. Unlike most of the video compression standards,

JPEG supports a variety of color spaces including RGB and YCrCb.

JPEG2000

JPEG2000 is a new still image coding standard from the ISO that was adopted in December 2000 [2]. It was

targeted at many of the same applications as JPEG including high-quality digital still cameras, hard copy

devices and Internet picture applications. The primary goals were to provide improved compression along

with more seamless quality and resolution scalability.

JPEG2000 achieves key improvements in scalability of resolution and bitrate through use of several key

functions that are not used by the JPEG, MPEG, and H.26x standards.

Discrete Wavelet Transform: The wavelet transform is used replacing the DCT to achieve higher

compression and improve support for scalable transmission. Wavelets are new basis functions, unlike the

usual cosines (DCT) and sines (FFT). They are called wavelets because they look like small waves. They

have an excellent ability to represent both stationary as well as transient phenomena with few coefficients.

Wavelets represent signals as a linear summation of shifted and translated versions of a basic wave.

JPEG2000 is coded in frequency sub-bands using the wavelet transform to allow resolution scalability. The

same bitstream can be decoded at different resolutions. Also, a thumbnail can be sent providing excellent

quality at lower resolution and the resolution can be gradually increased as more sub-bands are received. This

structure also helps improve error resilience for wireless and Internet applications.

Bit Plane Coding

: The quantized sub-bands from the wavelet transform are divided into code blocks. Code

blocks are entropy coded along bit planes using a combination of a bit plane coder and binary arithmetic

coding. In JPEG2000, embedded block coding with optimized truncation (EBCOT) is used to implement bit

plane coding. The algorithm uses symmetries and redundancies within and across the bit planes. The bit

plane coding structure can be used to offer bitrate scalability since increasing detail can be added as more bit

planes are decoded. Also, different quality bitstreams can be decoded at the same resolution, depending on

the client’s bandwidth without having to re-encode separately for each client.

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